{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data():\n",
    "    train = pd.read_csv(\"happiness_train_complete.csv\")\n",
    "    test = pd.read_csv(\"happiness_test_complete.csv\")\n",
    "    return train, test\n",
    "train, test = get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>happiness</th>\n",
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       "      <th>public_service_5</th>\n",
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       "      <td>2015/7/25 17:33</td>\n",
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       "      <td>2</td>\n",
       "      <td>1994</td>\n",
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       "      <td>67</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>78.0</td>\n",
       "      <td>60</td>\n",
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       "      <td>70</td>\n",
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       "      <th>7999</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8000 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  happiness  survey_type  province  city  county      survey_time  \\\n",
       "0        1          4            1        12    32      59   2015/8/4 14:18   \n",
       "1        2          4            2        18    52      85  2015/7/21 15:04   \n",
       "2        3          4            2        29    83     126  2015/7/21 13:24   \n",
       "3        4          5            2        10    28      51  2015/7/25 17:33   \n",
       "4        5          4            1         7    18      36   2015/8/10 9:50   \n",
       "...    ...        ...          ...       ...   ...     ...              ...   \n",
       "7995  7996          2            2        29    82     124  2015/7/21 19:36   \n",
       "7996  7997          3            1        12    32      61  2015/7/31 16:00   \n",
       "7997  7998          4            1        16    46      78   2015/8/1 17:48   \n",
       "7998  7999          3            1         1     1       8  2015/9/22 18:52   \n",
       "7999  8000          4            1         1     1       3  2015/9/28 20:22   \n",
       "\n",
       "      gender  birth  nationality  ...  neighbor_familiarity  public_service_1  \\\n",
       "0          1   1959            1  ...                     4                50   \n",
       "1          1   1992            1  ...                     3                90   \n",
       "2          2   1967            1  ...                     4                90   \n",
       "3          2   1943            1  ...                     3               100   \n",
       "4          2   1994            1  ...                     2                50   \n",
       "...      ...    ...          ...  ...                   ...               ...   \n",
       "7995       1   1981            1  ...                     3                40   \n",
       "7996       2   1945            1  ...                     4                80   \n",
       "7997       2   1967            1  ...                     4                75   \n",
       "7998       2   1978            1  ...                     2                56   \n",
       "7999       2   1991            1  ...                     3                80   \n",
       "\n",
       "      public_service_2 public_service_3  public_service_4  public_service_5  \\\n",
       "0                   60               50                50              30.0   \n",
       "1                   70               70                80              85.0   \n",
       "2                   80               75                79              80.0   \n",
       "3                   90               70                80              80.0   \n",
       "4                   50               50                50              50.0   \n",
       "...                ...              ...               ...               ...   \n",
       "7995                50               50                50              40.0   \n",
       "7996                80               80                80              80.0   \n",
       "7997                70               70                80              80.0   \n",
       "7998                67               70                69              78.0   \n",
       "7999                80               80                80              80.0   \n",
       "\n",
       "      public_service_6  public_service_7  public_service_8  public_service_9  \n",
       "0                   30                50                50                50  \n",
       "1                   70                90                60                60  \n",
       "2                   90                90                90                75  \n",
       "3                   90                90                80                80  \n",
       "4                   50                50                50                50  \n",
       "...                ...               ...               ...               ...  \n",
       "7995                50                50                60                50  \n",
       "7996                60                60                80                80  \n",
       "7997                70                75                70                75  \n",
       "7998                60                70                80                70  \n",
       "7999                80                80                80                80  \n",
       "\n",
       "[8000 rows x 140 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <td>-2</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2963</th>\n",
       "      <td>10964</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>117</td>\n",
       "      <td>2015/7/19 20:01</td>\n",
       "      <td>2</td>\n",
       "      <td>1946</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>10965</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>74</td>\n",
       "      <td>114</td>\n",
       "      <td>2015/8/8 13:09</td>\n",
       "      <td>2</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>50.0</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2965</th>\n",
       "      <td>10966</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>84</td>\n",
       "      <td>127</td>\n",
       "      <td>2015/7/22 9:29</td>\n",
       "      <td>2</td>\n",
       "      <td>1968</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2966</th>\n",
       "      <td>10967</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>54</td>\n",
       "      <td>2015/7/20 16:06</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>10968</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>68</td>\n",
       "      <td>2015/9/3 9:59</td>\n",
       "      <td>1</td>\n",
       "      <td>1941</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2968 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  survey_type  province  city  county      survey_time  gender  \\\n",
       "0      8001            1         2     2       9  2015/7/24 10:30       2   \n",
       "1      8002            1        22    66     106  2015/7/12 15:38       2   \n",
       "2      8003            2         9    22      44    2015/7/5 9:36       2   \n",
       "3      8004            2        18    52      86  2015/7/19 10:10       2   \n",
       "4      8005            2        24    70     110   2015/8/3 11:41       1   \n",
       "...     ...          ...       ...   ...     ...              ...     ...   \n",
       "2963  10964            1        27    77     117  2015/7/19 20:01       2   \n",
       "2964  10965            2        26    74     114   2015/8/8 13:09       2   \n",
       "2965  10966            2        29    84     127   2015/7/22 9:29       2   \n",
       "2966  10967            1        11    31      54  2015/7/20 16:06       1   \n",
       "2967  10968            1        13    37      68    2015/9/3 9:59       1   \n",
       "\n",
       "      birth  nationality  religion  ...  neighbor_familiarity  \\\n",
       "0      1972            8         0  ...                     4   \n",
       "1      1938            1         1  ...                     5   \n",
       "2      1935            1         1  ...                     5   \n",
       "3      1992            1         1  ...                     4   \n",
       "4      1990            1         1  ...                    -8   \n",
       "...     ...          ...       ...  ...                   ...   \n",
       "2963   1946            1         1  ...                     5   \n",
       "2964   1977            1         1  ...                     3   \n",
       "2965   1968            1         1  ...                     4   \n",
       "2966   1950            1         1  ...                     3   \n",
       "2967   1941            1         1  ...                     5   \n",
       "\n",
       "      public_service_1 public_service_2  public_service_3  public_service_4  \\\n",
       "0                   80             80.0                60                80   \n",
       "1                   90             80.0                80                80   \n",
       "2                   95             95.0                80                90   \n",
       "3                   80             80.0                70                90   \n",
       "4                   60             50.0                 0                30   \n",
       "...                ...              ...               ...               ...   \n",
       "2963                50             60.0                60                70   \n",
       "2964                60             50.0                70                50   \n",
       "2965                60             60.0                60                60   \n",
       "2966                84             60.0                70                87   \n",
       "2967                90             90.0                90                90   \n",
       "\n",
       "      public_service_5  public_service_6  public_service_7  public_service_8  \\\n",
       "0                   80                80                80                80   \n",
       "1                   80                80                70                80   \n",
       "2                   80                95                95                80   \n",
       "3                   80                80                70                60   \n",
       "4                   40                50                60                -2   \n",
       "...                ...               ...               ...               ...   \n",
       "2963                50                60                40                60   \n",
       "2964                50                50                50                50   \n",
       "2965                60                60                60                60   \n",
       "2966                90                80                80                80   \n",
       "2967                90                90                90                90   \n",
       "\n",
       "      public_service_9  \n",
       "0                   80  \n",
       "1                   80  \n",
       "2                   90  \n",
       "3                   50  \n",
       "4                   60  \n",
       "...                ...  \n",
       "2963                50  \n",
       "2964                50  \n",
       "2965                60  \n",
       "2966                80  \n",
       "2967                90  \n",
       "\n",
       "[2968 rows x 139 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "happiness幸福值是要预测的目标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>happiness</th>\n",
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       "      <td>2015/7/21 13:24</td>\n",
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       "      <td>90</td>\n",
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       "      <td>2015/7/25 17:33</td>\n",
       "      <td>2</td>\n",
       "      <td>1943</td>\n",
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       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1977</td>\n",
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       "      <th>2965</th>\n",
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       "      <td>70</td>\n",
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       "      <td>80</td>\n",
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       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10968 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  happiness  survey_type  province  city  county      survey_time  \\\n",
       "0         1        4.0            1        12    32      59   2015/8/4 14:18   \n",
       "1         2        4.0            2        18    52      85  2015/7/21 15:04   \n",
       "2         3        4.0            2        29    83     126  2015/7/21 13:24   \n",
       "3         4        5.0            2        10    28      51  2015/7/25 17:33   \n",
       "4         5        4.0            1         7    18      36   2015/8/10 9:50   \n",
       "...     ...        ...          ...       ...   ...     ...              ...   \n",
       "2963  10964        NaN            1        27    77     117  2015/7/19 20:01   \n",
       "2964  10965        NaN            2        26    74     114   2015/8/8 13:09   \n",
       "2965  10966        NaN            2        29    84     127   2015/7/22 9:29   \n",
       "2966  10967        NaN            1        11    31      54  2015/7/20 16:06   \n",
       "2967  10968        NaN            1        13    37      68    2015/9/3 9:59   \n",
       "\n",
       "      gender  birth  nationality  ...  neighbor_familiarity  public_service_1  \\\n",
       "0          1   1959            1  ...                     4                50   \n",
       "1          1   1992            1  ...                     3                90   \n",
       "2          2   1967            1  ...                     4                90   \n",
       "3          2   1943            1  ...                     3               100   \n",
       "4          2   1994            1  ...                     2                50   \n",
       "...      ...    ...          ...  ...                   ...               ...   \n",
       "2963       2   1946            1  ...                     5                50   \n",
       "2964       2   1977            1  ...                     3                60   \n",
       "2965       2   1968            1  ...                     4                60   \n",
       "2966       1   1950            1  ...                     3                84   \n",
       "2967       1   1941            1  ...                     5                90   \n",
       "\n",
       "      public_service_2 public_service_3  public_service_4  public_service_5  \\\n",
       "0                 60.0               50                50              30.0   \n",
       "1                 70.0               70                80              85.0   \n",
       "2                 80.0               75                79              80.0   \n",
       "3                 90.0               70                80              80.0   \n",
       "4                 50.0               50                50              50.0   \n",
       "...                ...              ...               ...               ...   \n",
       "2963              60.0               60                70              50.0   \n",
       "2964              50.0               70                50              50.0   \n",
       "2965              60.0               60                60              60.0   \n",
       "2966              60.0               70                87              90.0   \n",
       "2967              90.0               90                90              90.0   \n",
       "\n",
       "      public_service_6  public_service_7  public_service_8  public_service_9  \n",
       "0                   30                50                50                50  \n",
       "1                   70                90                60                60  \n",
       "2                   90                90                90                75  \n",
       "3                   90                90                80                80  \n",
       "4                   50                50                50                50  \n",
       "...                ...               ...               ...               ...  \n",
       "2963                60                40                60                50  \n",
       "2964                50                50                50                50  \n",
       "2965                60                60                60                60  \n",
       "2966                80                80                80                80  \n",
       "2967                90                90                90                90  \n",
       "\n",
       "[10968 rows x 140 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([train, test])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>happiness</th>\n",
       "      <th>survey_type</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>survey_time</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth</th>\n",
       "      <th>nationality</th>\n",
       "      <th>religion</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbor_familiarity</th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>59</td>\n",
       "      <td>2015/8/4 14:18</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>52</td>\n",
       "      <td>85</td>\n",
       "      <td>2015/7/21 15:04</td>\n",
       "      <td>1</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>85.0</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>83</td>\n",
       "      <td>126</td>\n",
       "      <td>2015/7/21 13:24</td>\n",
       "      <td>2</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>80.0</td>\n",
       "      <td>75</td>\n",
       "      <td>79</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>51</td>\n",
       "      <td>2015/7/25 17:33</td>\n",
       "      <td>2</td>\n",
       "      <td>1943</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>2015/8/10 9:50</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>117</td>\n",
       "      <td>2015/7/19 20:01</td>\n",
       "      <td>2</td>\n",
       "      <td>1946</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10965</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>74</td>\n",
       "      <td>114</td>\n",
       "      <td>2015/8/8 13:09</td>\n",
       "      <td>2</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>50.0</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>84</td>\n",
       "      <td>127</td>\n",
       "      <td>2015/7/22 9:29</td>\n",
       "      <td>2</td>\n",
       "      <td>1968</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10967</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>54</td>\n",
       "      <td>2015/7/20 16:06</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>90.0</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10968</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>68</td>\n",
       "      <td>2015/9/3 9:59</td>\n",
       "      <td>1</td>\n",
       "      <td>1941</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10968 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       happiness  survey_type  province  city  county      survey_time  \\\n",
       "id                                                                       \n",
       "1            4.0            1        12    32      59   2015/8/4 14:18   \n",
       "2            4.0            2        18    52      85  2015/7/21 15:04   \n",
       "3            4.0            2        29    83     126  2015/7/21 13:24   \n",
       "4            5.0            2        10    28      51  2015/7/25 17:33   \n",
       "5            4.0            1         7    18      36   2015/8/10 9:50   \n",
       "...          ...          ...       ...   ...     ...              ...   \n",
       "10964        NaN            1        27    77     117  2015/7/19 20:01   \n",
       "10965        NaN            2        26    74     114   2015/8/8 13:09   \n",
       "10966        NaN            2        29    84     127   2015/7/22 9:29   \n",
       "10967        NaN            1        11    31      54  2015/7/20 16:06   \n",
       "10968        NaN            1        13    37      68    2015/9/3 9:59   \n",
       "\n",
       "       gender  birth  nationality  religion  ...  neighbor_familiarity  \\\n",
       "id                                           ...                         \n",
       "1           1   1959            1         1  ...                     4   \n",
       "2           1   1992            1         1  ...                     3   \n",
       "3           2   1967            1         0  ...                     4   \n",
       "4           2   1943            1         1  ...                     3   \n",
       "5           2   1994            1         1  ...                     2   \n",
       "...       ...    ...          ...       ...  ...                   ...   \n",
       "10964       2   1946            1         1  ...                     5   \n",
       "10965       2   1977            1         1  ...                     3   \n",
       "10966       2   1968            1         1  ...                     4   \n",
       "10967       1   1950            1         1  ...                     3   \n",
       "10968       1   1941            1         1  ...                     5   \n",
       "\n",
       "       public_service_1 public_service_2  public_service_3  public_service_4  \\\n",
       "id                                                                             \n",
       "1                    50             60.0                50                50   \n",
       "2                    90             70.0                70                80   \n",
       "3                    90             80.0                75                79   \n",
       "4                   100             90.0                70                80   \n",
       "5                    50             50.0                50                50   \n",
       "...                 ...              ...               ...               ...   \n",
       "10964                50             60.0                60                70   \n",
       "10965                60             50.0                70                50   \n",
       "10966                60             60.0                60                60   \n",
       "10967                84             60.0                70                87   \n",
       "10968                90             90.0                90                90   \n",
       "\n",
       "       public_service_5  public_service_6  public_service_7  public_service_8  \\\n",
       "id                                                                              \n",
       "1                  30.0                30                50                50   \n",
       "2                  85.0                70                90                60   \n",
       "3                  80.0                90                90                90   \n",
       "4                  80.0                90                90                80   \n",
       "5                  50.0                50                50                50   \n",
       "...                 ...               ...               ...               ...   \n",
       "10964              50.0                60                40                60   \n",
       "10965              50.0                50                50                50   \n",
       "10966              60.0                60                60                60   \n",
       "10967              90.0                80                80                80   \n",
       "10968              90.0                90                90                90   \n",
       "\n",
       "       public_service_9  \n",
       "id                       \n",
       "1                    50  \n",
       "2                    60  \n",
       "3                    75  \n",
       "4                    80  \n",
       "5                    50  \n",
       "...                 ...  \n",
       "10964                50  \n",
       "10965                50  \n",
       "10966                60  \n",
       "10967                80  \n",
       "10968                90  \n",
       "\n",
       "[10968 rows x 139 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_info_columns\", 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10968 entries, 1 to 10968\n",
      "Data columns (total 139 columns):\n",
      " #    Column                Non-Null Count  Dtype  \n",
      "---   ------                --------------  -----  \n",
      " 0    happiness             8000 non-null   float64\n",
      " 1    survey_type           10968 non-null  int64  \n",
      " 2    province              10968 non-null  int64  \n",
      " 3    city                  10968 non-null  int64  \n",
      " 4    county                10968 non-null  int64  \n",
      " 5    survey_time           10968 non-null  object \n",
      " 6    gender                10968 non-null  int64  \n",
      " 7    birth                 10968 non-null  int64  \n",
      " 8    nationality           10968 non-null  int64  \n",
      " 9    religion              10968 non-null  int64  \n",
      " 10   religion_freq         10968 non-null  int64  \n",
      " 11   edu                   10968 non-null  int64  \n",
      " 12   edu_other             6 non-null      object \n",
      " 13   edu_status            9399 non-null   float64\n",
      " 14   edu_yr                8212 non-null   float64\n",
      " 15   income                10968 non-null  int64  \n",
      " 16   political             10968 non-null  int64  \n",
      " 17   join_party            1126 non-null   float64\n",
      " 18   floor_area            10968 non-null  float64\n",
      " 19   property_0            10968 non-null  int64  \n",
      " 20   property_1            10968 non-null  int64  \n",
      " 21   property_2            10968 non-null  int64  \n",
      " 22   property_3            10968 non-null  int64  \n",
      " 23   property_4            10968 non-null  int64  \n",
      " 24   property_5            10968 non-null  int64  \n",
      " 25   property_6            10968 non-null  int64  \n",
      " 26   property_7            10968 non-null  int64  \n",
      " 27   property_8            10968 non-null  int64  \n",
      " 28   property_other        89 non-null     object \n",
      " 29   height_cm             10968 non-null  int64  \n",
      " 30   weight_jin            10968 non-null  int64  \n",
      " 31   health                10968 non-null  int64  \n",
      " 32   health_problem        10968 non-null  int64  \n",
      " 33   depression            10968 non-null  int64  \n",
      " 34   hukou                 10968 non-null  int64  \n",
      " 35   hukou_loc             10964 non-null  float64\n",
      " 36   media_1               10968 non-null  int64  \n",
      " 37   media_2               10968 non-null  int64  \n",
      " 38   media_3               10968 non-null  int64  \n",
      " 39   media_4               10968 non-null  int64  \n",
      " 40   media_5               10968 non-null  int64  \n",
      " 41   media_6               10968 non-null  int64  \n",
      " 42   leisure_1             10968 non-null  int64  \n",
      " 43   leisure_2             10968 non-null  int64  \n",
      " 44   leisure_3             10968 non-null  int64  \n",
      " 45   leisure_4             10968 non-null  int64  \n",
      " 46   leisure_5             10968 non-null  int64  \n",
      " 47   leisure_6             10968 non-null  int64  \n",
      " 48   leisure_7             10968 non-null  int64  \n",
      " 49   leisure_8             10968 non-null  int64  \n",
      " 50   leisure_9             10968 non-null  int64  \n",
      " 51   leisure_10            10968 non-null  int64  \n",
      " 52   leisure_11            10968 non-null  int64  \n",
      " 53   leisure_12            10968 non-null  int64  \n",
      " 54   socialize             10968 non-null  int64  \n",
      " 55   relax                 10968 non-null  int64  \n",
      " 56   learn                 10968 non-null  int64  \n",
      " 57   social_neighbor       9871 non-null   float64\n",
      " 58   social_friend         9871 non-null   float64\n",
      " 59   socia_outing          10968 non-null  int64  \n",
      " 60   equity                10968 non-null  int64  \n",
      " 61   class                 10968 non-null  int64  \n",
      " 62   class_10_before       10968 non-null  int64  \n",
      " 63   class_10_after        10968 non-null  int64  \n",
      " 64   class_14              10968 non-null  int64  \n",
      " 65   work_exper            10968 non-null  int64  \n",
      " 66   work_status           4029 non-null   float64\n",
      " 67   work_yr               4029 non-null   float64\n",
      " 68   work_type             4030 non-null   float64\n",
      " 69   work_manage           4030 non-null   float64\n",
      " 70   insur_1               10968 non-null  int64  \n",
      " 71   insur_2               10968 non-null  int64  \n",
      " 72   insur_3               10968 non-null  int64  \n",
      " 73   insur_4               10968 non-null  int64  \n",
      " 74   family_income         10967 non-null  float64\n",
      " 75   family_m              10968 non-null  int64  \n",
      " 76   family_status         10968 non-null  int64  \n",
      " 77   house                 10968 non-null  int64  \n",
      " 78   car                   10968 non-null  int64  \n",
      " 79   invest_0              10968 non-null  int64  \n",
      " 80   invest_1              10968 non-null  int64  \n",
      " 81   invest_2              10968 non-null  int64  \n",
      " 82   invest_3              10968 non-null  int64  \n",
      " 83   invest_4              10968 non-null  int64  \n",
      " 84   invest_5              10968 non-null  int64  \n",
      " 85   invest_6              10968 non-null  int64  \n",
      " 86   invest_7              10968 non-null  int64  \n",
      " 87   invest_8              10968 non-null  int64  \n",
      " 88   invest_other          45 non-null     object \n",
      " 89   son                   10968 non-null  int64  \n",
      " 90   daughter              10968 non-null  int64  \n",
      " 91   minor_child           9520 non-null   float64\n",
      " 92   marital               10968 non-null  int64  \n",
      " 93   marital_1st           9839 non-null   float64\n",
      " 94   s_birth               8601 non-null   float64\n",
      " 95   marital_now           8521 non-null   float64\n",
      " 96   s_edu                 8601 non-null   float64\n",
      " 97   s_political           8601 non-null   float64\n",
      " 98   s_hukou               8601 non-null   float64\n",
      " 99   s_income              8601 non-null   float64\n",
      " 100  s_work_exper          8601 non-null   float64\n",
      " 101  s_work_status         3524 non-null   float64\n",
      " 102  s_work_type           3524 non-null   float64\n",
      " 103  f_birth               10968 non-null  int64  \n",
      " 104  f_edu                 10968 non-null  int64  \n",
      " 105  f_political           10968 non-null  int64  \n",
      " 106  f_work_14             10968 non-null  int64  \n",
      " 107  m_birth               10968 non-null  int64  \n",
      " 108  m_edu                 10968 non-null  int64  \n",
      " 109  m_political           10968 non-null  int64  \n",
      " 110  m_work_14             10968 non-null  int64  \n",
      " 111  status_peer           10968 non-null  int64  \n",
      " 112  status_3_before       10968 non-null  int64  \n",
      " 113  view                  10968 non-null  int64  \n",
      " 114  inc_ability           10968 non-null  int64  \n",
      " 115  inc_exp               10968 non-null  float64\n",
      " 116  trust_1               10968 non-null  int64  \n",
      " 117  trust_2               10968 non-null  int64  \n",
      " 118  trust_3               10968 non-null  int64  \n",
      " 119  trust_4               10968 non-null  int64  \n",
      " 120  trust_5               10968 non-null  int64  \n",
      " 121  trust_6               10968 non-null  int64  \n",
      " 122  trust_7               10968 non-null  int64  \n",
      " 123  trust_8               10968 non-null  int64  \n",
      " 124  trust_9               10968 non-null  int64  \n",
      " 125  trust_10              10968 non-null  int64  \n",
      " 126  trust_11              10968 non-null  int64  \n",
      " 127  trust_12              10968 non-null  int64  \n",
      " 128  trust_13              10968 non-null  int64  \n",
      " 129  neighbor_familiarity  10968 non-null  int64  \n",
      " 130  public_service_1      10968 non-null  int64  \n",
      " 131  public_service_2      10968 non-null  float64\n",
      " 132  public_service_3      10968 non-null  int64  \n",
      " 133  public_service_4      10968 non-null  int64  \n",
      " 134  public_service_5      10968 non-null  float64\n",
      " 135  public_service_6      10968 non-null  int64  \n",
      " 136  public_service_7      10968 non-null  int64  \n",
      " 137  public_service_8      10968 non-null  int64  \n",
      " 138  public_service_9      10968 non-null  int64  \n",
      "dtypes: float64(27), int64(108), object(4)\n",
      "memory usage: 11.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#重复值\n",
    "data.duplicated().sum()  # 没有重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu_other           10962\n",
       "invest_other        10923\n",
       "property_other      10879\n",
       "join_party           9842\n",
       "s_work_type          7444\n",
       "                    ...  \n",
       "leisure_9               0\n",
       "leisure_8               0\n",
       "leisure_7               0\n",
       "leisure_6               0\n",
       "public_service_9        0\n",
       "Length: 139, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#缺失值\n",
    "data.isnull().sum().sort_values(ascending=False)  # 使用树模型不处理缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类别数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survey_time', 'edu_other', 'property_other', 'invest_other'], dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ_cols = data.select_dtypes(include='object').columns\n",
    "categ_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:, 'time'] = pd.to_datetime(data.loc[:,'survey_time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 教育信息 edu、edu_status 、edu_yr 、edu_other  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 4     3084\n",
       " 3     2546\n",
       " 1     1479\n",
       " 6     1300\n",
       " 12     669\n",
       " 10     535\n",
       " 7      484\n",
       " 9      250\n",
       " 11     211\n",
       " 5      122\n",
       " 13     108\n",
       " 2       87\n",
       " 8       64\n",
       "-8       19\n",
       " 14      10\n",
       "Name: edu, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['edu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -8是异常值，用众数4表示初中学历填充\n",
    "data.loc[:,'edu'].replace(-8, 4, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_f(feat):\n",
    "    \"\"\"\n",
    "    绘制某种特征的计数统计柱状图\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(20,10))\n",
    "    sns.countplot(x=feat, data=data, hue='happiness',ax=ax)\n",
    "    plt.show()\n",
    "\n",
    "plot_f('edu')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0       1488\n",
       " 2013.0     219\n",
       " 1980.0     178\n",
       " 2014.0     160\n",
       " 1978.0     158\n",
       "           ... \n",
       " 1941.0       2\n",
       " 1944.0       1\n",
       " 1937.0       1\n",
       " 1934.0       1\n",
       " 1935.0       1\n",
       "Name: edu_yr, Length: 83, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['edu_yr'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0    1488\n",
       "-1.0     145\n",
       "-3.0      46\n",
       "Name: edu_yr, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 存在异常值，\n",
    "data[data['edu_yr']<0]['edu_yr'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>happiness</th>\n",
       "      <th>survey_type</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>survey_time</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth</th>\n",
       "      <th>nationality</th>\n",
       "      <th>religion</th>\n",
       "      <th>...</th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "      <td>109</td>\n",
       "      <td>2015/9/23 19:12</td>\n",
       "      <td>1</td>\n",
       "      <td>1968</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>20.0</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80</td>\n",
       "      <td>62</td>\n",
       "      <td>52</td>\n",
       "      <td>75</td>\n",
       "      <td>2015-09-23 19:12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>41</td>\n",
       "      <td>72</td>\n",
       "      <td>2015/7/15 15:05</td>\n",
       "      <td>2</td>\n",
       "      <td>1935</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>70</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2</td>\n",
       "      <td>80</td>\n",
       "      <td>-2</td>\n",
       "      <td>80</td>\n",
       "      <td>2015-07-15 15:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>2015/7/5 8:08</td>\n",
       "      <td>2</td>\n",
       "      <td>1948</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>2015-07-05 08:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>87</td>\n",
       "      <td>2015/7/15 15:24</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>79.0</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>80.0</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>2015-07-15 15:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>2015/7/6 9:44</td>\n",
       "      <td>2</td>\n",
       "      <td>1937</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>2015-07-06 09:44:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10930</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>107</td>\n",
       "      <td>2015/7/5 16:56</td>\n",
       "      <td>2</td>\n",
       "      <td>1944</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>2015-07-05 16:56:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10935</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>2015/8/1 9:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>70.0</td>\n",
       "      <td>75</td>\n",
       "      <td>77</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>2015-08-01 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10955</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>57</td>\n",
       "      <td>91</td>\n",
       "      <td>2015/7/17 15:06</td>\n",
       "      <td>2</td>\n",
       "      <td>1940</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>2015-07-17 15:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10963</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2015/7/31 13:46</td>\n",
       "      <td>2</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>2015-07-31 13:46:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>84</td>\n",
       "      <td>127</td>\n",
       "      <td>2015/7/22 9:29</td>\n",
       "      <td>2</td>\n",
       "      <td>1968</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>2015-07-22 09:29:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1479 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       happiness  survey_type  province  city  county      survey_time  \\\n",
       "id                                                                       \n",
       "12           4.0            2        23    69     109  2015/9/23 19:12   \n",
       "15           4.0            2        15    41      72  2015/7/15 15:05   \n",
       "22           5.0            2         9    21      43    2015/7/5 8:08   \n",
       "30           5.0            2        18    53      87  2015/7/15 15:24   \n",
       "35           4.0            2         9    21      43    2015/7/6 9:44   \n",
       "...          ...          ...       ...   ...     ...              ...   \n",
       "10930        NaN            2        22    67     107   2015/7/5 16:56   \n",
       "10935        NaN            2         6    12      28    2015/8/1 9:00   \n",
       "10955        NaN            1        19    57      91  2015/7/17 15:06   \n",
       "10963        NaN            2         2     4      11  2015/7/31 13:46   \n",
       "10966        NaN            2        29    84     127   2015/7/22 9:29   \n",
       "\n",
       "       gender  birth  nationality  religion  ...  public_service_1  \\\n",
       "id                                           ...                     \n",
       "12          1   1968            1         0  ...               100   \n",
       "15          2   1935            1         0  ...                -2   \n",
       "22          2   1948            1         1  ...               100   \n",
       "30          1   1947            1         1  ...                80   \n",
       "35          2   1937            1         0  ...                70   \n",
       "...       ...    ...          ...       ...  ...               ...   \n",
       "10930       2   1944            1         0  ...               100   \n",
       "10935       2   1957            1         1  ...                80   \n",
       "10955       2   1940            1         1  ...                -2   \n",
       "10963       2   1988            1         1  ...                70   \n",
       "10966       2   1968            1         1  ...                60   \n",
       "\n",
       "       public_service_2 public_service_3  public_service_4  public_service_5  \\\n",
       "id                                                                             \n",
       "12                 20.0               60                70              60.0   \n",
       "15                 80.0               70                -2              -2.0   \n",
       "22                100.0              100               100             100.0   \n",
       "30                 79.0               89                78              80.0   \n",
       "35                 80.0               80                80              80.0   \n",
       "...                 ...              ...               ...               ...   \n",
       "10930              80.0              100               100             100.0   \n",
       "10935              70.0               75                77              75.0   \n",
       "10955              -2.0               -2                -2              -2.0   \n",
       "10963              70.0               60                70              60.0   \n",
       "10966              60.0               60                60              60.0   \n",
       "\n",
       "       public_service_6  public_service_7  public_service_8  public_service_9  \\\n",
       "id                                                                              \n",
       "12                   80                62                52                75   \n",
       "15                   -2                80                -2                80   \n",
       "22                  100               100               100               100   \n",
       "30                   68                80                80                79   \n",
       "35                   80                70                80                80   \n",
       "...                 ...               ...               ...               ...   \n",
       "10930               100                70               100               100   \n",
       "10935                80                60                70                60   \n",
       "10955                -2                -2                -2                -2   \n",
       "10963                70                70                70                60   \n",
       "10966                60                60                60                60   \n",
       "\n",
       "                     time  \n",
       "id                         \n",
       "12    2015-09-23 19:12:00  \n",
       "15    2015-07-15 15:05:00  \n",
       "22    2015-07-05 08:08:00  \n",
       "30    2015-07-15 15:24:00  \n",
       "35    2015-07-06 09:44:00  \n",
       "...                   ...  \n",
       "10930 2015-07-05 16:56:00  \n",
       "10935 2015-08-01 09:00:00  \n",
       "10955 2015-07-17 15:06:00  \n",
       "10963 2015-07-31 13:46:00  \n",
       "10966 2015-07-22 09:29:00  \n",
       "\n",
       "[1479 rows x 140 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['edu']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1479"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['edu']==1]['edu_yr'].isnull().sum()  # 没有受过教育的人的完成最高学历年份为空"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     781\n",
       "3     488\n",
       "6     176\n",
       "7      59\n",
       "10     52\n",
       "12     33\n",
       "9      32\n",
       "11     22\n",
       "5      18\n",
       "8      10\n",
       "14      5\n",
       "13      3\n",
       "Name: edu, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['edu_yr']<0]['edu'].value_counts()  # 完成学历年份异常的人的学历情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# edu_yr,最高学历完成年份，对于异常值用其出生年+学历填充，比如初中学历，受教育年限9年，假设都是6岁入学，就是出生年分＋15\n",
    "edu_years = {\n",
    "    3:6,  # 小学\n",
    "    4:9,  # 初中\n",
    "    5:12, # 职业高中\n",
    "    6:12, # 普高\n",
    "    7:12, # 中专\n",
    "    8:12, # 技校\n",
    "    9:17, # 专科（成人）\n",
    "    10:15,# 专科\n",
    "    11:18,# 本科（成人）\n",
    "    12:16,# 本科\n",
    "    13:19,# 研究生\n",
    "    14:12\n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([    1,     3,     8,    24,    31,    37,    38,    40,    44,\n",
       "               54,\n",
       "            ...\n",
       "            10884, 10912, 10921, 10938, 10945, 10949, 10950, 10954, 10965,\n",
       "            10967],\n",
       "           dtype='int64', name='id', length=1679)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = data[data['edu_yr']<0].index\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[index, 'edu_yr'] = data.loc[index]['edu'].apply(lambda x: edu_years[int(x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>happiness</th>\n",
       "      <th>survey_type</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>survey_time</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth</th>\n",
       "      <th>nationality</th>\n",
       "      <th>religion</th>\n",
       "      <th>...</th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [happiness, survey_type, province, city, county, survey_time, gender, birth, nationality, religion, religion_freq, edu, edu_other, edu_status, edu_yr, income, political, join_party, floor_area, property_0, property_1, property_2, property_3, property_4, property_5, property_6, property_7, property_8, property_other, height_cm, weight_jin, health, health_problem, depression, hukou, hukou_loc, media_1, media_2, media_3, media_4, media_5, media_6, leisure_1, leisure_2, leisure_3, leisure_4, leisure_5, leisure_6, leisure_7, leisure_8, leisure_9, leisure_10, leisure_11, leisure_12, socialize, relax, learn, social_neighbor, social_friend, socia_outing, equity, class, class_10_before, class_10_after, class_14, work_exper, work_status, work_yr, work_type, work_manage, insur_1, insur_2, insur_3, insur_4, family_income, family_m, family_status, house, car, invest_0, invest_1, invest_2, invest_3, invest_4, invest_5, invest_6, invest_7, invest_8, invest_other, son, daughter, minor_child, marital, marital_1st, s_birth, marital_now, s_edu, s_political, s_hukou, s_income, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 140 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['edu_yr']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[index, 'edu_yr'] = data.loc[index, 'birth']+ data.loc[index, 'edu_yr']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013.0    221\n",
       "1980.0    219\n",
       "1979.0    194\n",
       "1978.0    186\n",
       "1981.0    179\n",
       "         ... \n",
       "1937.0      4\n",
       "1935.0      4\n",
       "1933.0      3\n",
       "1932.0      2\n",
       "1931.0      1\n",
       "Name: edu_yr, Length: 85, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['edu_yr'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,20))\n",
    "sns.countplot(y='edu_yr', data=data, hue='happiness', ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "夜校           4\n",
       "受访者回答当过兵     1\n",
       "中共中央干部训练班    1\n",
       "Name: edu_other, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['edu_other'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只有6条数据，删去该特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('edu_other', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### political, join_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    9224\n",
       " 4    1133\n",
       " 2     548\n",
       "-8      47\n",
       " 3      16\n",
       "Name: political, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#political\n",
    "data['political'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# political政治面貌，出现异常值-8用1群众代替\n",
    "data.loc[:, 'political'].replace(-8, 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0       108\n",
       " 2012.0     32\n",
       " 1976.0     30\n",
       " 1998.0     27\n",
       " 1985.0     27\n",
       "          ... \n",
       " 1952.0      2\n",
       " 1944.0      2\n",
       " 1946.0      1\n",
       " 1950.0      1\n",
       " 1947.0      1\n",
       "Name: join_party, Length: 72, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  join_party\n",
    "data['join_party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1126.000000\n",
       "mean     1775.782416\n",
       "std       614.524572\n",
       "min        -3.000000\n",
       "25%      1970.000000\n",
       "50%      1985.000000\n",
       "75%      2001.000000\n",
       "max      2015.000000\n",
       "Name: join_party, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['join_party'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0    108\n",
       "-3.0     12\n",
       "Name: join_party, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['join_party']<0]['join_party'].value_counts()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用出生年份+20填充\n",
    "index = data[data['join_party']<0].index\n",
    "data.loc[index, 'join_party'] = data.loc[index, 'birth'] + 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>happiness</th>\n",
       "      <th>survey_type</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>survey_time</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth</th>\n",
       "      <th>nationality</th>\n",
       "      <th>religion</th>\n",
       "      <th>...</th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [happiness, survey_type, province, city, county, survey_time, gender, birth, nationality, religion, religion_freq, edu, edu_status, edu_yr, income, political, join_party, floor_area, property_0, property_1, property_2, property_3, property_4, property_5, property_6, property_7, property_8, property_other, height_cm, weight_jin, health, health_problem, depression, hukou, hukou_loc, media_1, media_2, media_3, media_4, media_5, media_6, leisure_1, leisure_2, leisure_3, leisure_4, leisure_5, leisure_6, leisure_7, leisure_8, leisure_9, leisure_10, leisure_11, leisure_12, socialize, relax, learn, social_neighbor, social_friend, socia_outing, equity, class, class_10_before, class_10_after, class_14, work_exper, work_status, work_yr, work_type, work_manage, insur_1, insur_2, insur_3, insur_4, family_income, family_m, family_status, house, car, invest_0, invest_1, invest_2, invest_3, invest_4, invest_5, invest_6, invest_7, invest_8, invest_other, son, daughter, minor_child, marital, marital_1st, s_birth, marital_now, s_edu, s_political, s_hukou, s_income, s_work_exper, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 139 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['join_party']<0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### property_0-8，property_othe，floor_area\n",
    "\n",
    "房产信息\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_0</th>\n",
       "      <th>property_1</th>\n",
       "      <th>property_2</th>\n",
       "      <th>property_3</th>\n",
       "      <th>property_4</th>\n",
       "      <th>property_5</th>\n",
       "      <th>property_6</th>\n",
       "      <th>property_7</th>\n",
       "      <th>property_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.008023</td>\n",
       "      <td>0.471827</td>\n",
       "      <td>0.267141</td>\n",
       "      <td>0.100839</td>\n",
       "      <td>0.104030</td>\n",
       "      <td>0.025438</td>\n",
       "      <td>0.004012</td>\n",
       "      <td>0.022429</td>\n",
       "      <td>0.134664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.089217</td>\n",
       "      <td>0.499228</td>\n",
       "      <td>0.442487</td>\n",
       "      <td>0.301129</td>\n",
       "      <td>0.305313</td>\n",
       "      <td>0.157457</td>\n",
       "      <td>0.063213</td>\n",
       "      <td>0.148080</td>\n",
       "      <td>0.341380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_0    property_1    property_2    property_3    property_4  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       0.008023      0.471827      0.267141      0.100839      0.104030   \n",
       "std        0.089217      0.499228      0.442487      0.301129      0.305313   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "         property_5    property_6    property_7    property_8  \n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  \n",
       "mean       0.025438      0.004012      0.022429      0.134664  \n",
       "std        0.157457      0.063213      0.148080      0.341380  \n",
       "min        0.000000      0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      0.000000      0.000000  \n",
       "50%        0.000000      0.000000      0.000000      0.000000  \n",
       "75%        0.000000      0.000000      0.000000      0.000000  \n",
       "max        1.000000      1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propertys = ['property_'+str(i) for i in range(9)]\n",
    "data.loc[:,propertys].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_0</th>\n",
       "      <th>property_1</th>\n",
       "      <th>property_2</th>\n",
       "      <th>property_3</th>\n",
       "      <th>property_4</th>\n",
       "      <th>property_5</th>\n",
       "      <th>property_6</th>\n",
       "      <th>property_7</th>\n",
       "      <th>property_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10965</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10967</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10968</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10968 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       property_0  property_1  property_2  property_3  property_4  property_5  \\\n",
       "id                                                                              \n",
       "1               0           1           0           0           0           0   \n",
       "2               0           0           0           0           1           0   \n",
       "3               0           1           1           0           0           0   \n",
       "4               0           0           0           1           0           0   \n",
       "5               0           0           0           0           1           0   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "10964           0           0           0           0           0           0   \n",
       "10965           0           1           0           0           0           0   \n",
       "10966           0           0           1           0           0           0   \n",
       "10967           0           1           0           0           0           0   \n",
       "10968           0           1           1           0           0           0   \n",
       "\n",
       "       property_6  property_7  property_8  \n",
       "id                                         \n",
       "1               0           0           0  \n",
       "2               0           0           0  \n",
       "3               0           0           0  \n",
       "4               0           0           0  \n",
       "5               0           0           0  \n",
       "...           ...         ...         ...  \n",
       "10964           0           0           1  \n",
       "10965           0           0           0  \n",
       "10966           0           0           0  \n",
       "10967           0           0           0  \n",
       "10968           0           0           0  \n",
       "\n",
       "[10968 rows x 9 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:,propertys]  # 这9个属性经过了one-hot编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### property_other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "没有房产证                    8\n",
       "家人共有                     7\n",
       "无产权                      4\n",
       "未过户                      2\n",
       "家庭成员共有                   2\n",
       "                        ..\n",
       "没有房产证。                   1\n",
       "共同                       1\n",
       "原为配偶所有，配偶刚去世，还未办理转移手续    1\n",
       "才买，还无房产证                 1\n",
       "但目前房产证还没办下来              1\n",
       "Name: property_other, Length: 66, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['property_other'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,20))\n",
    "sns.countplot(y='property_other', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['property_other'].count() # 89条数据, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data.loc[:,propertys].sum(axis=1)==0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一列property_9记录是否有property_other的信息\n",
    "data.loc[:, 'property_9'] = data.loc[:,'property_other'].apply(lambda x: 1 if pd.notnull(x) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10879\n",
       "1       89\n",
       "Name: property_9, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['property_9'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### floor_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10968.000000\n",
       "mean       115.983901\n",
       "std         90.882269\n",
       "min          0.000000\n",
       "25%         64.600000\n",
       "50%         97.800000\n",
       "75%        134.000000\n",
       "max       2400.000000\n",
       "Name: floor_area, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['floor_area'].describe()  # 套内面积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='floor_area'>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['floor_area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 身体健康信息 height_cm,weight_jin, health, health_problem, depression            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='height_cm'>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['height_cm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='happiness', y='height_cm', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='happiness', y='weight_jin', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**看上去幸福感和身高体重没什么关系**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 4    4257\n",
       " 3    2379\n",
       " 5    2359\n",
       " 2    1617\n",
       " 1     349\n",
       "-8       7\n",
       "Name: health, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['health'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7个异常值用众数4填充\n",
    "data.loc[:,'health'].replace(-8,4,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='health', ylabel='count'>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='health', hue='happiness', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5和3人数差不多的情况下，幸福感更高的比例更高**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 4    4010\n",
       " 5    3502\n",
       " 3    1997\n",
       " 2    1094\n",
       " 1     313\n",
       "-8      52\n",
       "Name: health_problem, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# health_problem\n",
    "data['health_problem'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:,'health_problem'].replace(-8,4,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='health_problem', ylabel='count'>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='health_problem', hue='happiness', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 4    4644\n",
       " 5    2783\n",
       " 3    2623\n",
       " 2     763\n",
       " 1     129\n",
       "-8      26\n",
       "Name: depression, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# depression\n",
    "data['depression'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='depression', ylabel='count'>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[:,'depression'].replace(-8,4,inplace=True)\n",
    "sns.countplot(x='depression', hue='happiness', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5和3人数差不多的情况下，幸福感更高的比例更高**\n",
    "\n",
    "幸福感和健康水平有一定关联"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 户口信息34   hukou、 hukou_loc\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    6194\n",
       "2    2877\n",
       "5    1139\n",
       "4     737\n",
       "8       8\n",
       "6       6\n",
       "7       4\n",
       "3       3\n",
       "Name: hukou, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['hukou'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**受访者农业户口的比例更高**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    8049\n",
       "2.0    1801\n",
       "3.0    1074\n",
       "4.0      40\n",
       "Name: hukou_loc, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['hukou_loc'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 媒体信息 media_1-6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>media_1</th>\n",
       "      <th>media_2</th>\n",
       "      <th>media_3</th>\n",
       "      <th>media_4</th>\n",
       "      <th>media_5</th>\n",
       "      <th>media_6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10965</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10967</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10968</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10968 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       media_1  media_2  media_3  media_4  media_5  media_6\n",
       "id                                                         \n",
       "1            4        2        5        5        4        3\n",
       "2            2        2        1        3        5        1\n",
       "3            2        2        2        5        1        3\n",
       "4            2        1        1        5        1        1\n",
       "5            1        3        4        2        5        5\n",
       "...        ...      ...      ...      ...      ...      ...\n",
       "10964        2        2        3        4        1        1\n",
       "10965        1        1        1        4        1        1\n",
       "10966        1        1        1        4        1        1\n",
       "10967        1        1        1        5        1        1\n",
       "10968        4        2        1        4        1        1\n",
       "\n",
       "[10968 rows x 6 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medias = ['media_'+str(i) for i in range(1,7)]\n",
    "data[medias]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "media_1    0\n",
       "media_2    0\n",
       "media_3    0\n",
       "media_4    0\n",
       "media_5    0\n",
       "media_6    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[medias].isnull().sum()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### leisure 闲暇时间活动"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leisure_1</th>\n",
       "      <th>leisure_2</th>\n",
       "      <th>leisure_3</th>\n",
       "      <th>leisure_4</th>\n",
       "      <th>leisure_5</th>\n",
       "      <th>leisure_6</th>\n",
       "      <th>leisure_7</th>\n",
       "      <th>leisure_8</th>\n",
       "      <th>leisure_9</th>\n",
       "      <th>leisure_10</th>\n",
       "      <th>leisure_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.00000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.619712</td>\n",
       "      <td>4.511214</td>\n",
       "      <td>3.447028</td>\n",
       "      <td>3.762126</td>\n",
       "      <td>4.407914</td>\n",
       "      <td>3.754103</td>\n",
       "      <td>3.557349</td>\n",
       "      <td>3.585522</td>\n",
       "      <td>3.513311</td>\n",
       "      <td>4.71116</td>\n",
       "      <td>4.489606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.055012</td>\n",
       "      <td>0.947545</td>\n",
       "      <td>1.220894</td>\n",
       "      <td>1.455657</td>\n",
       "      <td>1.401422</td>\n",
       "      <td>0.938319</td>\n",
       "      <td>1.233182</td>\n",
       "      <td>1.568641</td>\n",
       "      <td>1.629604</td>\n",
       "      <td>1.02245</td>\n",
       "      <td>1.234028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.00000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          leisure_1     leisure_2     leisure_3     leisure_4     leisure_5  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       1.619712      4.511214      3.447028      3.762126      4.407914   \n",
       "std        1.055012      0.947545      1.220894      1.455657      1.401422   \n",
       "min       -8.000000     -8.000000     -8.000000     -8.000000     -8.000000   \n",
       "25%        1.000000      4.000000      3.000000      3.000000      4.000000   \n",
       "50%        1.000000      5.000000      3.000000      4.000000      5.000000   \n",
       "75%        2.000000      5.000000      4.000000      5.000000      5.000000   \n",
       "max        5.000000      5.000000      5.000000      5.000000      5.000000   \n",
       "\n",
       "          leisure_6     leisure_7     leisure_8     leisure_9   leisure_10  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.00000   \n",
       "mean       3.754103      3.557349      3.585522      3.513311      4.71116   \n",
       "std        0.938319      1.233182      1.568641      1.629604      1.02245   \n",
       "min       -8.000000     -8.000000     -8.000000     -8.000000     -8.00000   \n",
       "25%        3.000000      3.000000      2.000000      2.000000      5.00000   \n",
       "50%        4.000000      4.000000      4.000000      4.000000      5.00000   \n",
       "75%        4.000000      4.000000      5.000000      5.000000      5.00000   \n",
       "max        5.000000      5.000000      5.000000      5.000000      5.00000   \n",
       "\n",
       "         leisure_11  \n",
       "count  10968.000000  \n",
       "mean       4.489606  \n",
       "std        1.234028  \n",
       "min       -8.000000  \n",
       "25%        4.000000  \n",
       "50%        5.000000  \n",
       "75%        5.000000  \n",
       "max        5.000000  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leisures = ['leisure_'+str(i) for i in range(1,12)]\n",
    "data[leisures].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leisure_1</th>\n",
       "      <th>leisure_2</th>\n",
       "      <th>leisure_3</th>\n",
       "      <th>leisure_4</th>\n",
       "      <th>leisure_5</th>\n",
       "      <th>leisure_6</th>\n",
       "      <th>leisure_7</th>\n",
       "      <th>leisure_8</th>\n",
       "      <th>leisure_9</th>\n",
       "      <th>leisure_10</th>\n",
       "      <th>leisure_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   leisure_1  leisure_2  leisure_3  leisure_4  leisure_5  leisure_6  \\\n",
       "0          1          5          3          5          5          4   \n",
       "\n",
       "   leisure_7  leisure_8  leisure_9  leisure_10  leisure_11  \n",
       "0          4          5          5           5           5  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[leisures].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in leisures:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leisure_1</th>\n",
       "      <th>leisure_2</th>\n",
       "      <th>leisure_3</th>\n",
       "      <th>leisure_4</th>\n",
       "      <th>leisure_5</th>\n",
       "      <th>leisure_6</th>\n",
       "      <th>leisure_7</th>\n",
       "      <th>leisure_8</th>\n",
       "      <th>leisure_9</th>\n",
       "      <th>leisure_10</th>\n",
       "      <th>leisure_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.625456</td>\n",
       "      <td>4.537290</td>\n",
       "      <td>3.468089</td>\n",
       "      <td>3.781090</td>\n",
       "      <td>4.516958</td>\n",
       "      <td>3.777079</td>\n",
       "      <td>3.605489</td>\n",
       "      <td>3.613968</td>\n",
       "      <td>3.545314</td>\n",
       "      <td>4.766867</td>\n",
       "      <td>4.546499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.026741</td>\n",
       "      <td>0.763961</td>\n",
       "      <td>1.113379</td>\n",
       "      <td>1.385271</td>\n",
       "      <td>0.814569</td>\n",
       "      <td>0.784525</td>\n",
       "      <td>0.991619</td>\n",
       "      <td>1.473251</td>\n",
       "      <td>1.527639</td>\n",
       "      <td>0.591795</td>\n",
       "      <td>0.915420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          leisure_1     leisure_2     leisure_3     leisure_4     leisure_5  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       1.625456      4.537290      3.468089      3.781090      4.516958   \n",
       "std        1.026741      0.763961      1.113379      1.385271      0.814569   \n",
       "min        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "25%        1.000000      4.000000      3.000000      3.000000      4.000000   \n",
       "50%        1.000000      5.000000      3.000000      4.000000      5.000000   \n",
       "75%        2.000000      5.000000      4.000000      5.000000      5.000000   \n",
       "max        5.000000      5.000000      5.000000      5.000000      5.000000   \n",
       "\n",
       "          leisure_6     leisure_7     leisure_8     leisure_9    leisure_10  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       3.777079      3.605489      3.613968      3.545314      4.766867   \n",
       "std        0.784525      0.991619      1.473251      1.527639      0.591795   \n",
       "min        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "25%        3.000000      3.000000      2.000000      2.000000      5.000000   \n",
       "50%        4.000000      4.000000      4.000000      4.000000      5.000000   \n",
       "75%        4.000000      4.000000      5.000000      5.000000      5.000000   \n",
       "max        5.000000      5.000000      5.000000      5.000000      5.000000   \n",
       "\n",
       "         leisure_11  \n",
       "count  10968.000000  \n",
       "mean       4.546499  \n",
       "std        0.915420  \n",
       "min        1.000000  \n",
       "25%        4.000000  \n",
       "50%        5.000000  \n",
       "75%        5.000000  \n",
       "max        5.000000  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[leisures].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### socialize, relax, learn, social_neighbor, social_friend, socia_outing, equity                  \n",
    "                 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>socialize</th>\n",
       "      <th>relax</th>\n",
       "      <th>learn</th>\n",
       "      <th>social_neighbor</th>\n",
       "      <th>social_friend</th>\n",
       "      <th>socia_outing</th>\n",
       "      <th>equity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>9871.000000</td>\n",
       "      <td>9871.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.799690</td>\n",
       "      <td>3.297411</td>\n",
       "      <td>1.924690</td>\n",
       "      <td>3.452538</td>\n",
       "      <td>3.607335</td>\n",
       "      <td>1.812363</td>\n",
       "      <td>3.131838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.076999</td>\n",
       "      <td>1.070670</td>\n",
       "      <td>1.188598</td>\n",
       "      <td>2.079219</td>\n",
       "      <td>2.004808</td>\n",
       "      <td>1.565850</td>\n",
       "      <td>1.315518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          socialize         relax         learn  social_neighbor  \\\n",
       "count  10968.000000  10968.000000  10968.000000      9871.000000   \n",
       "mean       2.799690      3.297411      1.924690         3.452538   \n",
       "std        1.076999      1.070670      1.188598         2.079219   \n",
       "min       -8.000000     -8.000000     -8.000000        -8.000000   \n",
       "25%        2.000000      3.000000      1.000000         2.000000   \n",
       "50%        3.000000      3.000000      2.000000         3.000000   \n",
       "75%        4.000000      4.000000      3.000000         5.000000   \n",
       "max        5.000000      5.000000      5.000000         7.000000   \n",
       "\n",
       "       social_friend  socia_outing        equity  \n",
       "count    9871.000000  10968.000000  10968.000000  \n",
       "mean        3.607335      1.812363      3.131838  \n",
       "std         2.004808      1.565850      1.315518  \n",
       "min        -8.000000     -8.000000     -8.000000  \n",
       "25%         2.000000      1.000000      2.000000  \n",
       "50%         3.000000      1.000000      3.000000  \n",
       "75%         5.000000      2.000000      4.000000  \n",
       "max         7.000000      6.000000      5.000000  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['socialize', 'relax', 'learn', 'social_neighbor', 'social_friend', 'socia_outing', 'equity']\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>socialize</th>\n",
       "      <th>relax</th>\n",
       "      <th>learn</th>\n",
       "      <th>social_neighbor</th>\n",
       "      <th>social_friend</th>\n",
       "      <th>socia_outing</th>\n",
       "      <th>equity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>9871.000000</td>\n",
       "      <td>9871.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.805160</td>\n",
       "      <td>3.321481</td>\n",
       "      <td>1.947666</td>\n",
       "      <td>3.469760</td>\n",
       "      <td>3.674197</td>\n",
       "      <td>1.870624</td>\n",
       "      <td>3.201860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.047109</td>\n",
       "      <td>0.943776</td>\n",
       "      <td>1.078397</td>\n",
       "      <td>2.024989</td>\n",
       "      <td>1.788294</td>\n",
       "      <td>1.352566</td>\n",
       "      <td>1.003463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          socialize         relax         learn  social_neighbor  \\\n",
       "count  10968.000000  10968.000000  10968.000000      9871.000000   \n",
       "mean       2.805160      3.321481      1.947666         3.469760   \n",
       "std        1.047109      0.943776      1.078397         2.024989   \n",
       "min        1.000000      1.000000      1.000000         1.000000   \n",
       "25%        2.000000      3.000000      1.000000         2.000000   \n",
       "50%        3.000000      3.000000      2.000000         3.000000   \n",
       "75%        4.000000      4.000000      3.000000         5.000000   \n",
       "max        5.000000      5.000000      5.000000         7.000000   \n",
       "\n",
       "       social_friend  socia_outing        equity  \n",
       "count    9871.000000  10968.000000  10968.000000  \n",
       "mean        3.674197      1.870624      3.201860  \n",
       "std         1.788294      1.352566      1.003463  \n",
       "min         1.000000      1.000000      1.000000  \n",
       "25%         2.000000      1.000000      2.000000  \n",
       "50%         3.000000      1.000000      4.000000  \n",
       "75%         5.000000      2.000000      4.000000  \n",
       "max         7.000000      6.000000      5.000000  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 阶层 class, class_10_before, class_10_after, class_14        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>class_10_before</th>\n",
       "      <th>class_10_after</th>\n",
       "      <th>class_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.197301</td>\n",
       "      <td>3.446116</td>\n",
       "      <td>4.444475</td>\n",
       "      <td>2.924052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.027329</td>\n",
       "      <td>2.169867</td>\n",
       "      <td>3.633429</td>\n",
       "      <td>2.276095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              class  class_10_before  class_10_after      class_14\n",
       "count  10968.000000     10968.000000    10968.000000  10968.000000\n",
       "mean       4.197301         3.446116        4.444475      2.924052\n",
       "std        2.027329         2.169867        3.633429      2.276095\n",
       "min       -8.000000        -8.000000       -8.000000     -8.000000\n",
       "25%        3.000000         2.000000        4.000000      2.000000\n",
       "50%        5.000000         4.000000        5.000000      3.000000\n",
       "75%        5.000000         5.000000        6.000000      4.000000\n",
       "max       10.000000        10.000000       10.000000     10.000000"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = ['class', 'class_10_before', 'class_10_after', 'class_14']\n",
    "data[classes].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in classes:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>class_10_before</th>\n",
       "      <th>class_10_after</th>\n",
       "      <th>class_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.322939</td>\n",
       "      <td>3.594548</td>\n",
       "      <td>5.200675</td>\n",
       "      <td>3.098195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.631731</td>\n",
       "      <td>1.709076</td>\n",
       "      <td>1.907530</td>\n",
       "      <td>1.756937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              class  class_10_before  class_10_after      class_14\n",
       "count  10968.000000     10968.000000    10968.000000  10968.000000\n",
       "mean       4.322939         3.594548        5.200675      3.098195\n",
       "std        1.631731         1.709076        1.907530      1.756937\n",
       "min        1.000000         1.000000        1.000000      1.000000\n",
       "25%        3.000000         2.000000        4.000000      2.000000\n",
       "50%        5.000000         4.000000        5.000000      3.000000\n",
       "75%        5.000000         5.000000        6.000000      4.000000\n",
       "max       10.000000        10.000000       10.000000     10.000000"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[classes].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 工作信息 work_exper, work_status, work_yr, work_type,work_manage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_exper</th>\n",
       "      <th>work_status</th>\n",
       "      <th>work_yr</th>\n",
       "      <th>work_type</th>\n",
       "      <th>work_manage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>4029.000000</td>\n",
       "      <td>4029.000000</td>\n",
       "      <td>4030.000000</td>\n",
       "      <td>4030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.984044</td>\n",
       "      <td>3.144949</td>\n",
       "      <td>14.447009</td>\n",
       "      <td>0.908189</td>\n",
       "      <td>2.638710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.752649</td>\n",
       "      <td>1.756344</td>\n",
       "      <td>11.397224</td>\n",
       "      <td>1.406120</td>\n",
       "      <td>1.763925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         work_exper  work_status      work_yr    work_type  work_manage\n",
       "count  10968.000000  4029.000000  4029.000000  4030.000000  4030.000000\n",
       "mean       2.984044     3.144949    14.447009     0.908189     2.638710\n",
       "std        1.752649     1.756344    11.397224     1.406120     1.763925\n",
       "min        1.000000    -8.000000    -3.000000    -8.000000    -8.000000\n",
       "25%        1.000000     3.000000     5.000000     1.000000     2.000000\n",
       "50%        3.000000     3.000000    12.000000     1.000000     3.000000\n",
       "75%        5.000000     3.000000    22.000000     1.000000     3.000000\n",
       "max        6.000000     9.000000    55.000000     2.000000     4.000000"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['work_exper', 'work_status', 'work_yr', 'work_type','work_manage']\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_exper</th>\n",
       "      <th>work_status</th>\n",
       "      <th>work_yr</th>\n",
       "      <th>work_type</th>\n",
       "      <th>work_manage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>4029.000000</td>\n",
       "      <td>4029.000000</td>\n",
       "      <td>4030.000000</td>\n",
       "      <td>4030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.984044</td>\n",
       "      <td>3.243237</td>\n",
       "      <td>14.447009</td>\n",
       "      <td>1.115881</td>\n",
       "      <td>2.876179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.752649</td>\n",
       "      <td>1.401838</td>\n",
       "      <td>11.397224</td>\n",
       "      <td>0.320122</td>\n",
       "      <td>0.783488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         work_exper  work_status      work_yr    work_type  work_manage\n",
       "count  10968.000000  4029.000000  4029.000000  4030.000000  4030.000000\n",
       "mean       2.984044     3.243237    14.447009     1.115881     2.876179\n",
       "std        1.752649     1.401838    11.397224     0.320122     0.783488\n",
       "min        1.000000     1.000000    -3.000000     1.000000     1.000000\n",
       "25%        1.000000     3.000000     5.000000     1.000000     2.000000\n",
       "50%        3.000000     3.000000    12.000000     1.000000     3.000000\n",
       "75%        5.000000     3.000000    22.000000     1.000000     3.000000\n",
       "max        6.000000     9.000000    55.000000     2.000000     4.000000"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0    108\n",
       "-1.0     85\n",
       "-3.0     16\n",
       "Name: work_yr, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['work_yr']<0]['work_yr'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保险信息，insur_1-4  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>insur_1</th>\n",
       "      <th>insur_2</th>\n",
       "      <th>insur_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.063366</td>\n",
       "      <td>1.236324</td>\n",
       "      <td>1.821663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.513248</td>\n",
       "      <td>0.802763</td>\n",
       "      <td>0.801725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            insur_1       insur_2       insur_3\n",
       "count  10968.000000  10968.000000  10968.000000\n",
       "mean       1.063366      1.236324      1.821663\n",
       "std        0.513248      0.802763      0.801725\n",
       "min       -8.000000     -8.000000     -8.000000\n",
       "25%        1.000000      1.000000      2.000000\n",
       "50%        1.000000      1.000000      2.000000\n",
       "75%        1.000000      2.000000      2.000000\n",
       "max        2.000000      2.000000      2.000000"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['insur_'+str(i) for i in range(1,4)]\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 家庭信息 family_m, family_status, house, car, son, daughter, minor_child\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family_m</th>\n",
       "      <th>family_status</th>\n",
       "      <th>house</th>\n",
       "      <th>car</th>\n",
       "      <th>son</th>\n",
       "      <th>daughter</th>\n",
       "      <th>minor_child</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>9520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.881747</td>\n",
       "      <td>2.589989</td>\n",
       "      <td>1.068381</td>\n",
       "      <td>1.817925</td>\n",
       "      <td>0.926240</td>\n",
       "      <td>0.780999</td>\n",
       "      <td>0.450945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.504973</td>\n",
       "      <td>1.081657</td>\n",
       "      <td>1.200844</td>\n",
       "      <td>0.505590</td>\n",
       "      <td>0.941787</td>\n",
       "      <td>0.985532</td>\n",
       "      <td>0.814377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           family_m  family_status         house           car           son  \\\n",
       "count  10968.000000   10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       2.881747       2.589989      1.068381      1.817925      0.926240   \n",
       "std        1.504973       1.081657      1.200844      0.505590      0.941787   \n",
       "min       -3.000000      -8.000000     -3.000000     -8.000000     -8.000000   \n",
       "25%        2.000000       2.000000      1.000000      2.000000      0.000000   \n",
       "50%        3.000000       3.000000      1.000000      2.000000      1.000000   \n",
       "75%        4.000000       3.000000      1.000000      2.000000      1.000000   \n",
       "max       50.000000       5.000000     96.000000      2.000000      8.000000   \n",
       "\n",
       "           daughter  minor_child  \n",
       "count  10968.000000  9520.000000  \n",
       "mean       0.780999     0.450945  \n",
       "std        0.985532     0.814377  \n",
       "min       -8.000000    -8.000000  \n",
       "25%        0.000000     0.000000  \n",
       "50%        1.000000     0.000000  \n",
       "75%        1.000000     1.000000  \n",
       "max       10.000000     6.000000  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['family_m', 'family_status', 'house', 'car', 'son', 'daughter', 'minor_child']\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 2     3690\n",
       " 3     2840\n",
       " 4     1570\n",
       " 1     1396\n",
       " 5      903\n",
       " 6      342\n",
       " 7      115\n",
       " 8       49\n",
       " 9       16\n",
       "-1       16\n",
       "-3       15\n",
       " 10       7\n",
       " 11       5\n",
       "-2        2\n",
       " 50       1\n",
       " 13       1\n",
       "Name: family_m, dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['family_m'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1     8593\n",
       " 2     1245\n",
       " 0      707\n",
       " 3      174\n",
       "-3       90\n",
       "-1       61\n",
       " 4       46\n",
       "-2       23\n",
       " 5       16\n",
       " 6        2\n",
       " 7        2\n",
       " 8        2\n",
       " 11       2\n",
       " 10       1\n",
       " 14       1\n",
       " 30       1\n",
       " 96       1\n",
       " 12       1\n",
       "Name: house, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['house'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:,'house'] = data.loc[:,'house'].apply(lambda x: x if x>0 else 0)  # 房子的异常值用0填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:,'family_m'] = data.loc[:,'family_m'].apply(lambda x: x if x>0 else 1)  # 家庭人数异常值用1填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family_m</th>\n",
       "      <th>family_status</th>\n",
       "      <th>house</th>\n",
       "      <th>car</th>\n",
       "      <th>son</th>\n",
       "      <th>daughter</th>\n",
       "      <th>minor_child</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>9520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.890682</td>\n",
       "      <td>2.654176</td>\n",
       "      <td>1.102753</td>\n",
       "      <td>1.829778</td>\n",
       "      <td>0.937728</td>\n",
       "      <td>0.792670</td>\n",
       "      <td>0.461870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.483871</td>\n",
       "      <td>0.715808</td>\n",
       "      <td>1.131187</td>\n",
       "      <td>0.375845</td>\n",
       "      <td>0.886073</td>\n",
       "      <td>0.927112</td>\n",
       "      <td>0.752218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           family_m  family_status         house           car           son  \\\n",
       "count  10968.000000   10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       2.890682       2.654176      1.102753      1.829778      0.937728   \n",
       "std        1.483871       0.715808      1.131187      0.375845      0.886073   \n",
       "min        1.000000       1.000000      0.000000      1.000000      0.000000   \n",
       "25%        2.000000       2.000000      1.000000      2.000000      0.000000   \n",
       "50%        3.000000       3.000000      1.000000      2.000000      1.000000   \n",
       "75%        4.000000       3.000000      1.000000      2.000000      1.000000   \n",
       "max       50.000000       5.000000     96.000000      2.000000      8.000000   \n",
       "\n",
       "           daughter  minor_child  \n",
       "count  10968.000000  9520.000000  \n",
       "mean       0.792670     0.461870  \n",
       "std        0.927112     0.752218  \n",
       "min        0.000000     0.000000  \n",
       "25%        0.000000     0.000000  \n",
       "50%        1.000000     0.000000  \n",
       "75%        1.000000     1.000000  \n",
       "max       10.000000     6.000000  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "sns.countplot(x='family_m', hue='happiness', data=data,ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    5939\n",
       "2    3516\n",
       "4     879\n",
       "1     606\n",
       "5      28\n",
       "Name: family_status, dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['family_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='family_status', hue='happiness', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAECCAYAAAD+VKAWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXC0lEQVR4nO3de7RWdb3v8fc3oo1yUUIuKiI29Jg7LxioXBawYMDxgowjxTDNI1YaZ6u4bdvFXZq6HaLW3u7cx0tKoKKVQac8FYmdRbgWEBFCirf06OigAW5CVEiTQPqdP54JclkPLux55nxYz/s1xhprru+czOcLLNaH3/zN+XsipYQkSR8ougFJUm0wECRJgIEgScoYCJIkwECQJGU+WHQD79dBBx2U+vfvX3QbkrRPWb58+asppZ6t7dtnA6F///4sW7as6DYkaZ8SES+V2+clI0kSYCBIkjIGgiQJ2IfnEKS9tWXLFlatWsWmTZuKbqUiOnXqRN++fenYsWPRraidMBBUN1atWkXXrl3p378/EVF0O3+TlBLr169n1apVHHHEEUW3o3bCS0aqG5s2baJHjx77fBgARAQ9evRoN6Md1QYDQXWlPYTBNu3p96LaYCBIkgADQXXsuuuuo7m5uaLn/MIXvlDR80l5clJ5H9cyYmTZfSMXtOTYiQBuvfXWoluQ3jdHCKprTU1NjBw5kgEDBrBq1SrOOOMMRo8ezWc/+1mgNIo4/fTTGTlyJBMnTuSdd97hvvvuY9SoUYwaNYpTTz2VDRs2bD9fY2Pj9u3m5mYuvPBCTjvtNI455hh++ctfAnDttdfS0NCw06+98soraWhoYNiwYbz00ktla1I1GQiqay+++CItLS18+tOfZsaMGVx66aXMnTuXlStXsnbtWgCGDx9OS0sLvXv35ic/+QkARx11FI8++ihjxoxh+vTpZc/f0tLCD3/4Q2bOnMmsWbN44oknWLBgAYsWLWLcuHHMnDkTgB/84Ac0Nzdz991386c//alsTaomLxmprk2aNAmAXr16ATB9+nTuvfdeXnvtNd5++20ABg4cCMDxxx/PypUr6dGjx061OXPmlD3/hAkT6Nq1K7169WLz5s08//zz/P73v6exsZFNmzZx6qmnAjB16lTGjx9Ply5duOWWW8rWpGpyhKC61rlz5+3bX//615k4cSIPPvjgTvWlS5cC8Pjjj3PkkUeWrb3X+QGOPvpoGhsbaW5uZvr06Zx88sn8+c9/Zt26dcydO5exY8fyne98p9WaVG2OEKTM5z//eW666SbuuusuAFavXg3AY489RmNjI3369GH8+PHcf//9/OEPf2DUqFF06tSJ2bNnt/k1BgwYwGGHHcbIkSPZvHkzd999N/vvvz8vvPACw4YNY9OmTUybNq3VmlRtkVIquof3ZdCgQcn3Q/Auo73xu9/9jmOOOWavfs11111HY2PjTpPF9913HwCf+cxnKtfc+/R+fk+qbxGxPKU0qLV9jhCkPbjuuut2q9VCEEjV4ByCJAkwECRJGQNBkgQ4h6A6N/DL91f0fMv/dVJFzyflyRGCJAkwEKRCrF27luHDh5fdv2XLFs4880yGDh3KPffck2NnqmcGgpSz119/nQsuuIC33nqr7DG33XYbgwYNYvHixcyZM8e1jJQLA0HKWYcOHZg1axbdunUre0xzczNnn302AEOHDsWHMJUHJ5WlnO0pCLZ56623OPTQQ7cfv23lVamaHCFINahLly7bV1t98803+etf/1pwR6oHjhBU12r1NtGBAweyaNEiJk6cyIoVKxg8eHDRLakOGAhSwebPn8+zzz7LlClTttcuuOACzjjjDBYuXMizzz7LKaecUmCHqhdeMpIK0tzcDMDo0aN3CgOAww8/nKamJoYNG8a8efPo0KFDAR2q3jhCkGrUIYccsv1OIykPjhAkSUAVAiEiDoiIuRHRFBEPRcSHImJGRCyOiKt3OK5NNUlSPqpxyeg84N9TSk0R8W3gHKBDSmloRNwZEUcBx7WlllJ6oQr9Sdu9fP1xFT1fv2uequj5pDxVPBBSSnfu8GVP4L8Dt2ZfzwcagBOB2W2o7RQIETEZmAzQr1+/SrcuSXWtanMIETEE6A78AVidlTcCvYHObaztJKU0LaU0KKU0qGfPntVqXaqaDRs2cPrppzN27FgmTJjA5s2bWz3uwgsvZOjQodxwww05d6h6VpVAiIgPA7cBnwPeBPbLdnXJXrOtNald+d73vscVV1xBU1MTffr04ZFHHtntmB//+Mds3bqVxYsXs2bNGl54wSunykc1JpU/ROnSz1dTSi8Byyld/gE4AVi5FzWpXbnkkksYO3YsAOvWraNXr167HbPjwnajR49m0aJFufao+lWNSeULgYHAVRFxFXAvcH5EHAKcDgwGErCwDTWpXfr1r3/N66+/3uqSFLsubPfiiy/m3Z7qVDUmlb8NfHvHWkT8FBgLfDOltCGrNbalJrU3r732Gpdddhk/+tGPWt3vwnYqSi5PKqeUXufdO4j2qiZVU963iW7evJmzzz6bm266icMPP7zVY7YtbDd48GBWrFjB0UcfnWuPql8uXSHlaMaMGSxfvpypU6cydepURo0axZYtW3a6m+iss85i+PDhrFmzhrlz57JkyZICO1Y9MRCkHF188cVcfPHFezymW7duNDc309TUxFe+8hUOOOCAnLpTvTMQpBrUvXt3F7ZT7rzXX5IEGAiSpIyXjFTXht02rKLn+9Vlv6ro+aQ8OUKQcvbaa6/R1NTEq6++WnQr0k4MBClHr7zyCuPGjWPp0qWMGjWKdevWtXqci9upCAaClKNnnnmGb33rW1x11VWceuqp/Pa3v93tGBe3U1EMBClHY8aMYfDgwSxYsIClS5cyZMiQ3Y5xcTsVxUCQcpZSYtasWXTs2JEOHTrstn/Xxe3Wrl2bd4uqUwaClLOI4I477mDo0KHMmTNnt/0ubqeieNup6lret4l+4xvf4OCDD2bSpEm88cYbHHjggbsd4+J2KoojBClHkydP5oEHHmDEiBFs3bqVvn37cvXVV+90zFlnncUDDzzAFVdcwezZsxk3blxB3areOEKQctS9e3eampp2qu16a6mL26koBoJUg1zcTkXwkpEkCTAQJEkZLxmprrWMGFnR841c0FLR80l5coQgSQIMBKkQa9eu5cQTTyy738XtVAQDQSrAl770pe1PI+/Kxe1UFANBytn8+fPp3Lkzffr0aXW/i9upKAaClKPNmzdz/fXXc/PNN5c9xsXtVBQDQcrRzTffzKWXXtrqGkbbuLidiuJtp6pred8mOm/ePObPn88dd9zBE088wUUXXcT06dN3OsbF7VQUA0HK0YIFC7ZvNzY2csUVV3D11VfvdDfRWWedxfDhw1mzZg1z585lyZIlRbSqOmQglLGnB5Z8+EiV0NzcDLi4nWqHgSDVIBe3UxGcVJYkAQaCJCnjJSPVtdu/+LOKnm/KLeMrej4pT44QJEmAgSDl6p133qFfv340NjbS2NjIU0891epx1157LSeddBJTpkzJuUPVMwNBytGTTz7JueeeS3NzM83NzRx33HG7HbNs2TIWLVrE0qVL6du3L/PmzSugU9UjA0HK0ZIlS3jooYdoaGjgvPPO45133tntmAULFvDJT36SiGDMmDEsXLiwgE5Vj6oSCBHROyIWZtuHRsSqiGjOPnpm9RkRsTgirt7h1+1Wk9qTk046iZaWFhYtWsSBBx7Iww8/vNsxLm6nolQ8ECKiOzAT6JyVTgGmppQas491EfEJoENKaShwSEQc1Vqt0r1JRTv++OM5+OCDAfjoRz/a6nsduLidilKN2063Ap8CfpJ9PRg4PSImAUtSSv8ENAKzs/3zgQbgxFZqO/1riYjJwGSAfv36VaF11Zu8bxM9//zzueqqqzj22GN56KGH+NrXvrbbMQMHDmT27Nmcc845rFixgv79++fao+pXxUcIKaWNKaUNO5TmAkNTSkOA/xIRx1MaPazO9m8Eepep7XruaSmlQSmlQT179qx061LVXXPNNZx//vkMGDCAIUOG8PGPf5yLLrpop2MaGhp4/PHHufzyy7n55ps599xzC+pW9SaPB9MWp5T+km0/BxwFvAnsl9W6UAqm1mpSu3Lsscfy5JNP7lTbdfnrD3zgA8ybN4+f//znXH755RxxxBF5tqg6lscP3V9ExMERsT9wKvA0sJzSJSGAE4CVZWpSRaWUim6hTfbbbz8mTpzIRz7ykbLH7Cu/F+078hgh/AvwKLAZuCul9HxEvAIsjIhDgNMpzTOkVmpSxXTq1In169fTo0cPIqLodv4mKSXWr19Pp06dim5F7UjVAiGl1Jh9fhT46C77NkZEIzAW+Oa2OYfWalKl9O3bl1WrVrFu3bqiW6mITp060bdv36LbUDtS2OJ2KaXXefeuorI1qVI6duzo9XhpD5y4lSQBBoIkKWMgSJIAA0GSlDEQJEmAgSBJyhgIkiTAQJAkZQwESRJgIEiSMgaCJAl4n4EQEQ3vfZQkaV/SpkCIiKZdSjdVoRdJUoH2uNpp9naXJwKHZu+JDKW3utxU7cYkSfl6rxFCtPJ5PXB21TqSJBVijyOElNIKYEVEHJ1Suj+nniRJBWjrG+TcGhHnAB/aVjAgJKl9aetdRo8AfSldMtr2IUlqR9o6QtiYUvq3qnYiSSpUWwNhUUQ8CNwPvAWQUlpQta4kSblrayBsAZ4DTqJ0uSgBBoIktSNtDYSVlEJgWxhIktqZvVm6IoD9gE8AI6rTjiSpKG0aIaSUZu7w5V0RcWeV+pEkFaRNgRARO44IugEfq047kqSitHUOYRTvzh1sBi6pTjuSpKK0dQ7hRmAt8GHgVeD5qnUkSSpEWwPhHqAXMBc4FLi3ah1JkgrR1ktGh6WUzs+2fxERLdVqSJJUjLYGwpqI+CrwG2AIsLp6LUmSitDWS0b/QCk8JgIbgf9RtY4kSYVoayB8F3g5pXQJ0JXSnIIkqR1payB03/ZwWkrpRuCg6rUkSSpCW+cQVkXElcBSSgvc/bF6LUmSitDWEcJngD9TmkN4G5hUrYYkScVo61pGfwFuq3IvkqQC7c1qp5KkdqwqgRARvSNiYbbdMSLmRMTiiPjc3tQkSfmpeCBERHdgJtA5K10GLEspDQXOjIiue1GTJOWkGiOErcCnKD3ABtAIzM62FwOD9qK2k4iYHBHLImLZunXrqtC6JNWvigdCSmljSmnDDqXOvLvUxUag917Udj33tJTSoJTSoJ49e1a6dUmqa3lMKr9J6a03Abpkr9nWmiQpJ3n80F0ONGTbJwAr96ImScpJW59U/lvMBB6OiOHA31NaMXV1G2uSpJxUbYSQUmrMPr8EjAV+BYxJKW1ta61avUmSdpfHCIGU0hrevYNor2qSpHw4cStJAgwESVLGQJAkAQaCJCljIEiSAANBkpQxECRJgIEgScoYCJIkwECQJGUMBEkSYCBIkjIGgiQJMBAkSRkDQZIEGAiSpIyBIEkCDARJUiaXt9CUWkaMLLtv5IKWHDuRVI4jBEkSYCBIkjIGgiQJcA6hXbv9iz8ru2/KLeNz7ETSvsARgiQJMBAkSRkDQZIEGAiSpIyBIEkCDARJUsZAkCQBBoIkKWMgSJIAA0GSlDEQJEmAgSBJyri43T5i2G3DWq3f6F+hpAqp+gghIj4YES9HRHP2cVxE/EtEPBYRt+9w3G41SVJ+8rhkdDzwYEqpMaXUCPwd0ACcDKyKiDERMWjXWg59SZJ2kMf1hsHAhIgYBrwErAB+lFJKETEPGA9saKU2b9cTRcRkYDJAv379cmhdkupHHiOEx4CRKaUG4A1gP2B1tm8j0Bvo3EptNymlaSmlQSmlQT179qxq05JUb/IYITyZUvpLtv0c8CFKoQDQhVIovdlKTZKUozx+8D4QESdERAdgAqXRQEO27wRgJbC8lZokKUd5jBCuB74PBPBT4AZgYUT8B3Ba9vEScNMuNUlSjqoeCCmlpyndabRddhfROOA/Ukr/r1xNkpSfQp5qSim9Dfyv96rVqtu/+LOy+6bcMj7HTiSpcpy8lSQBBoIkKVP3C+G4RpAklThCkCQBBoIkKWMgSJIAA0GSlDEQJEmAgSBJyhgIkiTAQJAkZQwESRJgIEiSMgaCJAkwECRJGQNBkgQYCJKkjIEgSQJ8P4Sa8vL1x5Xf2b1bfo1IqkuOECRJgCME1YDbv/izsvum3DI+x06k+uYIQZIEGAiSpIyXjFQxw24bVnbfjX6rSTXPEYIkCXCEIO2kZcTIsvtGLmjJsRMpf3URCN7fL0nvzUtGkiTAQJAkZerikpEqq+wlOC+/Sfs0RwiSJMBAkCRlDARJEuAcgtRm5RbhcwE+tReOECRJQDsaIQz88v1l9z3UNcdG3sO+0md75ppLUuv87pf2QS6xoWqouUCIiBnAMcDDKaUbiu6nXrWHkUy9Pi9RS3Md5YLL0KpNNRUIEfEJoENKaWhE3BkRR6WUXii6L6la9rTO1rl7CK68L23tqc9+1zy11+fzXfJqU6SUiu5hu4j4n8AjKaWHI2Ii0DWldO8O+ycDk7Mvjwaer3ALBwGvVvic1WCflWWflbMv9Aj13efhKaWere2oqREC0BlYnW1vBI7ccWdKaRowrVovHhHLUkqDqnX+SrHPyrLPytkXegT7LKfWbjt9E9gv2+5C7fUnSe1Wrf3AXQ40ZNsnACuLa0WS6kutXTL638DCiDgEOB0YnPPrV+1yVIXZZ2XZZ+XsCz2CfbaqpiaVASKiOzAWWJBS+s+i+5GkelFzgSBJKkatzSFIuYqID0fE2Ig4qOhepKIZCJmImBERiyPi6qJ72ZOI6B0RC4vuY08i4oCImBsRTRHxUER8qOieWhMRBwM/B04GHo2IVu/NrgXZ3/vjRfdRTkR8MCJejojm7KP8k2w1IHvwtWafgIuI7hHxcEQsjIi78npdA4Gdn5AGDomIo4ruqTXZ/MpMSs9r1LLzgH9PKY0F/hM4reB+yvkY8E8ppanAL4CPF9zPnvwb796SXYuOBx5MKTVmH3v/+HJOImI40CelVP5x6eKdD3w3pTQc6BoRuTyLYCCUNAKzs+35vHvra63ZCnyK0kN7NSuldGdKqSn7sifwxyL7KSelNC+ltCQiRlAaJfy66J5aExGjgbcohWutGgxMiIhFEfG9iKi1OxgBiIiOwHeAlRHx34ruZw/WA0dHxIHAYcDLebyogVCy6xPSvQvspayU0saU0oai+2iriBgCdE8pLSm6l3IiIiiF7BZKgVtTsstt1wD/XHQv7+ExYGRKqQF4Azij2HbKmgQ8C3wTODkiLiu4n3IWAUcB/wg8B7yex4saCCU+IV1hEfFh4Dbgc0X3siep5FJgMXBm0f204p+BO1JKbxTdyHt4MqX0Srb9HKUfZrXoRGBadkv7d4FRBfdTzo3AP6SUrqf05/nZPF7UH3wlPiFdQdn/amcDX00pvVR0P+VExJURMSn78kBK/7OtNWOASyOiGRgQEdML7qecByLihIjoAEwAVhTdUBkvAh/JtgcBtfr9uT9wXPbneQqQy/MBPocAREQ3YCHwS7InpGv50kxENKeUGovuo5yIuJjS/3C2/VD4dkppVoEttSqbpJ8N/B3wNHBpquF/ELX89x4RxwLfBwL4aUrpqoJbalVEdAXuoXRZuCMwMaW0es+/Kn8RcTJwL3A4pbmtCSmlN6v+ujX8/Z8rn5CWVO8MBEkS4ByCJCljIEiSAANBkpQxECRJgIEgScrU5Hoj0r4gIvYDfgh0A14FzgXuAw4BVlF6uvRrlO53bwAOAE7ztmbVKkcI0vv398BfU0ojKL3V4ZeBp1NKI4H/y7vLdhyZ1b4PjC6kU6kNDATp/fst8HRE/B9gPKWRwW+yfb8Bjsm2788+/xGoyfeGkMBAkP4WJwC/Sin9V6A78AylZaDJPj+Tbb9VQG/SXjMQpPdvJfCPEbEY6ENp9cyPRcQCSqt93ldca9Lec+kKSRLgCEGSlDEQJEmAgSBJyhgIkiTAQJAkZQwESRIA/x9xlCrqG66HEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='son', hue='happiness', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='daughter', hue='happiness', data=data)\n",
    "plt.legend(loc = 'upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 投资信息 invest_0-8， invest_other  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invest_0</th>\n",
       "      <th>invest_1</th>\n",
       "      <th>invest_2</th>\n",
       "      <th>invest_3</th>\n",
       "      <th>invest_4</th>\n",
       "      <th>invest_5</th>\n",
       "      <th>invest_6</th>\n",
       "      <th>invest_7</th>\n",
       "      <th>invest_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10965</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10967</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10968</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10968 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       invest_0  invest_1  invest_2  invest_3  invest_4  invest_5  invest_6  \\\n",
       "id                                                                            \n",
       "1             0         1         0         0         0         0         0   \n",
       "2             0         1         0         0         0         0         0   \n",
       "3             0         1         0         0         0         0         0   \n",
       "4             0         1         0         0         0         0         0   \n",
       "5             0         1         0         0         0         0         0   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "10964         0         1         0         0         0         0         0   \n",
       "10965         0         1         0         0         0         0         0   \n",
       "10966         0         1         0         0         0         0         0   \n",
       "10967         0         1         0         0         0         0         0   \n",
       "10968         0         1         0         0         0         0         0   \n",
       "\n",
       "       invest_7  invest_8  \n",
       "id                         \n",
       "1             0         0  \n",
       "2             0         0  \n",
       "3             0         0  \n",
       "4             0         0  \n",
       "5             0         0  \n",
       "...         ...       ...  \n",
       "10964         0         0  \n",
       "10965         0         0  \n",
       "10966         0         0  \n",
       "10967         0         0  \n",
       "10968         0         0  \n",
       "\n",
       "[10968 rows x 9 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['invest_'+str(i) for i in range(9)]\n",
    "data[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "银行存款                    7\n",
       "理财产品                    3\n",
       "储蓄存款                    3\n",
       "银行理财                    3\n",
       "理财                      2\n",
       "彩票                      2\n",
       "统筹                      1\n",
       "资本运作                    1\n",
       "商业保险                    1\n",
       "民间借贷                    1\n",
       "网上理财                    1\n",
       "自己没有，儿女不清楚              1\n",
       "投资开发区                   1\n",
       "余额宝                     1\n",
       "老人家不清楚                  1\n",
       "投资自己的工程                 1\n",
       "储蓄                      1\n",
       "高利贷                     1\n",
       "投资服务业、家具业               1\n",
       "福利车票                    1\n",
       "字画、茶壶                   1\n",
       "租房                      1\n",
       "银行存款利息                  1\n",
       "银行自动理财（自己不知道具体属于哪一种）    1\n",
       "家中有部分土地承包出去             1\n",
       "其他理财产品                  1\n",
       "个人融资                    1\n",
       "信鸽比赛                    1\n",
       "没有                      1\n",
       "活期储蓄                    1\n",
       "商业万能保险                  1\n",
       "Name: invest_other, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['invest_other'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='invest_other'>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5,10))\n",
    "sns.countplot(y='invest_other', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一列invest_9表示是否有invest_other信息\n",
    "data.loc[:,'invest_9'] = data.loc[:,'invest_other'].apply(lambda x: 1 if pd.notnull(x) else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 婚姻、配偶、父母信息\n",
    "marital, marital_1st, s_birth, marital_now, s_edu, s_political, s_hukou,  s_work_exper, s_work_status,s_work_type, f_birth, f_edu, f_political, f_work_14, m_birth, m_edu, m_political, m_work_14   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marital</th>\n",
       "      <th>marital_1st</th>\n",
       "      <th>s_birth</th>\n",
       "      <th>marital_now</th>\n",
       "      <th>s_edu</th>\n",
       "      <th>s_political</th>\n",
       "      <th>s_hukou</th>\n",
       "      <th>s_work_exper</th>\n",
       "      <th>s_work_status</th>\n",
       "      <th>s_work_type</th>\n",
       "      <th>f_birth</th>\n",
       "      <th>f_edu</th>\n",
       "      <th>f_political</th>\n",
       "      <th>f_work_14</th>\n",
       "      <th>m_birth</th>\n",
       "      <th>m_edu</th>\n",
       "      <th>m_political</th>\n",
       "      <th>m_work_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>9839.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>8521.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>3524.000000</td>\n",
       "      <td>3524.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.242159</td>\n",
       "      <td>1829.893790</td>\n",
       "      <td>1963.941053</td>\n",
       "      <td>1873.399484</td>\n",
       "      <td>4.607953</td>\n",
       "      <td>1.314266</td>\n",
       "      <td>1.822579</td>\n",
       "      <td>2.829903</td>\n",
       "      <td>3.187003</td>\n",
       "      <td>0.970772</td>\n",
       "      <td>1115.477206</td>\n",
       "      <td>2.011123</td>\n",
       "      <td>1.089442</td>\n",
       "      <td>2.736689</td>\n",
       "      <td>1154.928519</td>\n",
       "      <td>1.535740</td>\n",
       "      <td>0.895058</td>\n",
       "      <td>4.033826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.431988</td>\n",
       "      <td>534.815514</td>\n",
       "      <td>14.552396</td>\n",
       "      <td>464.026934</td>\n",
       "      <td>3.069316</td>\n",
       "      <td>1.051883</td>\n",
       "      <td>1.415909</td>\n",
       "      <td>1.730981</td>\n",
       "      <td>1.763940</td>\n",
       "      <td>1.194175</td>\n",
       "      <td>961.361182</td>\n",
       "      <td>3.690258</td>\n",
       "      <td>1.740775</td>\n",
       "      <td>4.209430</td>\n",
       "      <td>955.741708</td>\n",
       "      <td>3.083353</td>\n",
       "      <td>1.325904</td>\n",
       "      <td>5.531465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>1953.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>1964.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1922.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1926.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1947.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            marital  marital_1st      s_birth  marital_now        s_edu  \\\n",
       "count  10968.000000  9839.000000  8601.000000  8521.000000  8601.000000   \n",
       "mean       3.242159  1829.893790  1963.941053  1873.399484     4.607953   \n",
       "std        1.431988   534.815514    14.552396   464.026934     3.069316   \n",
       "min        1.000000    -3.000000  1907.000000    -3.000000    -8.000000   \n",
       "25%        3.000000  1970.000000  1953.000000  1975.000000     3.000000   \n",
       "50%        3.000000  1985.000000  1964.000000  1987.000000     4.000000   \n",
       "75%        3.000000  1997.000000  1975.000000  1999.000000     6.000000   \n",
       "max        7.000000  2015.000000  2004.000000  2015.000000    14.000000   \n",
       "\n",
       "       s_political      s_hukou  s_work_exper  s_work_status  s_work_type  \\\n",
       "count  8601.000000  8601.000000   8601.000000    3524.000000  3524.000000   \n",
       "mean      1.314266     1.822579      2.829903       3.187003     0.970772   \n",
       "std       1.051883     1.415909      1.730981       1.763940     1.194175   \n",
       "min      -8.000000    -8.000000      1.000000      -8.000000    -8.000000   \n",
       "25%       1.000000     1.000000      1.000000       3.000000     1.000000   \n",
       "50%       1.000000     1.000000      3.000000       3.000000     1.000000   \n",
       "75%       1.000000     2.000000      5.000000       3.000000     1.000000   \n",
       "max       4.000000     8.000000      6.000000       9.000000     2.000000   \n",
       "\n",
       "            f_birth         f_edu   f_political     f_work_14       m_birth  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean    1115.477206      2.011123      1.089442      2.736689   1154.928519   \n",
       "std      961.361182      3.690258      1.740775      4.209430    955.741708   \n",
       "min       -3.000000     -8.000000     -8.000000     -8.000000     -3.000000   \n",
       "25%       -2.000000      1.000000      1.000000      1.000000     -2.000000   \n",
       "50%     1922.000000      2.000000      1.000000      2.000000   1926.000000   \n",
       "75%     1947.000000      3.000000      1.000000      2.000000   1949.000000   \n",
       "max     1979.000000     14.000000      4.000000     17.000000   1985.000000   \n",
       "\n",
       "              m_edu   m_political     m_work_14  \n",
       "count  10968.000000  10968.000000  10968.000000  \n",
       "mean       1.535740      0.895058      4.033826  \n",
       "std        3.083353      1.325904      5.531465  \n",
       "min       -8.000000     -8.000000     -8.000000  \n",
       "25%        1.000000      1.000000      2.000000  \n",
       "50%        1.000000      1.000000      2.000000  \n",
       "75%        3.000000      1.000000      2.000000  \n",
       "max       14.000000      4.000000     17.000000  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['marital', 'marital_1st', 's_birth', 'marital_now', 's_edu', 's_political', 's_hukou', 's_work_exper', \n",
    "        's_work_status','s_work_type', 'f_birth', 'f_edu', 'f_political', 'f_work_14', 'm_birth', 'm_edu', 'm_political', 'm_work_14']\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0    603\n",
       "-3.0    130\n",
       "-1.0     39\n",
       "Name: marital_1st, dtype: int64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['marital_1st']<0]['marital_1st'].value_counts()  # 第一次结婚的时间，小于0的统一用-1填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0    419\n",
       "-3.0     61\n",
       "-1.0     11\n",
       "Name: marital_now, dtype: int64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['marital_now']<0]['marital_now'].value_counts()  # 现在的婚姻的结婚时间，小于0的统一用-1填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2    4492\n",
       "-3     172\n",
       "Name: f_birth, dtype: int64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['f_birth']<0]['f_birth'].value_counts()  # 父亲的出生时间 异常值数量很多不替换异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2    4281\n",
       "-3     167\n",
       "Name: m_birth, dtype: int64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['m_birth']<0]['m_birth'].value_counts()  # 母亲的出生时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)\n",
    "data.loc[:,'marital_1st'] = data.loc[:,'marital_1st'].apply(lambda x: x if x>0 else -1)\n",
    "data.loc[:,'marital_now'] = data.loc[:,'marital_now'].apply(lambda x: x if x>0 else -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 十年回顾信息 status_peer,status_3_before, view                 \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status_peer</th>\n",
       "      <th>status_3_before</th>\n",
       "      <th>view</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.226933</td>\n",
       "      <td>1.705689</td>\n",
       "      <td>3.290026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.960806</td>\n",
       "      <td>0.942302</td>\n",
       "      <td>2.039244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        status_peer  status_3_before          view\n",
       "count  10968.000000     10968.000000  10968.000000\n",
       "mean       2.226933         1.705689      3.290026\n",
       "std        0.960806         0.942302      2.039244\n",
       "min       -8.000000        -8.000000     -8.000000\n",
       "25%        2.000000         1.000000      3.000000\n",
       "50%        2.000000         2.000000      4.000000\n",
       "75%        3.000000         2.000000      4.000000\n",
       "max        3.000000         3.000000      5.000000"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['status_peer','status_3_before', 'view']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['status_peer','status_3_before', 'view']:\n",
    "    data.loc[:,col].replace(-8, data[col].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='status_peer', ylabel='count'>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='status_peer', hue='happiness', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**和同龄人相比越好越有幸福感，幸福果然是比出来的吗**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inc_ability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10968.000000\n",
       "mean         1.093363\n",
       "std          3.412580\n",
       "min         -8.000000\n",
       "25%          2.000000\n",
       "50%          2.000000\n",
       "75%          3.000000\n",
       "max          4.000000\n",
       "Name: inc_ability, dtype: float64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['inc_ability'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 2    6300\n",
       " 3    2879\n",
       "-8    1325\n",
       " 4     297\n",
       " 1     167\n",
       "Name: inc_ability, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['inc_ability'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:, 'inc_ability'].replace(-8, 2, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### trust_1-13   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trust_1</th>\n",
       "      <th>trust_2</th>\n",
       "      <th>trust_3</th>\n",
       "      <th>trust_4</th>\n",
       "      <th>trust_5</th>\n",
       "      <th>trust_6</th>\n",
       "      <th>trust_7</th>\n",
       "      <th>trust_8</th>\n",
       "      <th>trust_9</th>\n",
       "      <th>trust_10</th>\n",
       "      <th>trust_11</th>\n",
       "      <th>trust_12</th>\n",
       "      <th>trust_13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.790755</td>\n",
       "      <td>3.176149</td>\n",
       "      <td>2.144238</td>\n",
       "      <td>1.964533</td>\n",
       "      <td>4.172958</td>\n",
       "      <td>2.037381</td>\n",
       "      <td>2.615062</td>\n",
       "      <td>2.231583</td>\n",
       "      <td>2.496809</td>\n",
       "      <td>0.679887</td>\n",
       "      <td>-2.452042</td>\n",
       "      <td>-0.206783</td>\n",
       "      <td>1.755744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.476258</td>\n",
       "      <td>2.131744</td>\n",
       "      <td>3.971791</td>\n",
       "      <td>3.930545</td>\n",
       "      <td>1.226322</td>\n",
       "      <td>4.170531</td>\n",
       "      <td>2.034148</td>\n",
       "      <td>3.966369</td>\n",
       "      <td>2.784826</td>\n",
       "      <td>4.725945</td>\n",
       "      <td>5.565895</td>\n",
       "      <td>5.262257</td>\n",
       "      <td>1.686159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            trust_1       trust_2       trust_3       trust_4       trust_5  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       3.790755      3.176149      2.144238      1.964533      4.172958   \n",
       "std        1.476258      2.131744      3.971791      3.930545      1.226322   \n",
       "min       -8.000000     -8.000000     -8.000000     -8.000000     -8.000000   \n",
       "25%        3.000000      3.000000      3.000000      2.000000      4.000000   \n",
       "50%        4.000000      4.000000      4.000000      3.000000      4.000000   \n",
       "75%        4.000000      4.000000      4.000000      4.000000      5.000000   \n",
       "max        5.000000      5.000000      5.000000      5.000000      5.000000   \n",
       "\n",
       "            trust_6       trust_7       trust_8       trust_9      trust_10  \\\n",
       "count  10968.000000  10968.000000  10968.000000  10968.000000  10968.000000   \n",
       "mean       2.037381      2.615062      2.231583      2.496809      0.679887   \n",
       "std        4.170531      2.034148      3.966369      2.784826      4.725945   \n",
       "min       -8.000000     -8.000000     -8.000000     -8.000000     -8.000000   \n",
       "25%        3.000000      2.000000      3.000000      2.000000      1.000000   \n",
       "50%        4.000000      3.000000      4.000000      3.000000      3.000000   \n",
       "75%        4.000000      4.000000      4.000000      4.000000      4.000000   \n",
       "max        5.000000      5.000000      5.000000      5.000000      5.000000   \n",
       "\n",
       "           trust_11      trust_12      trust_13  \n",
       "count  10968.000000  10968.000000  10968.000000  \n",
       "mean      -2.452042     -0.206783      1.755744  \n",
       "std        5.565895      5.262257      1.686159  \n",
       "min       -8.000000     -8.000000     -8.000000  \n",
       "25%       -8.000000     -8.000000      1.000000  \n",
       "50%        1.000000      3.000000      2.000000  \n",
       "75%        3.000000      4.000000      3.000000  \n",
       "max        5.000000      5.000000      5.000000  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['trust_'+str(i) for i in range(1,14)]\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    data.loc[:,col].replace(-8, data.loc[:,col].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### neighbor_familiarity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 4    3621\n",
       " 5    3154\n",
       " 3    2562\n",
       " 2    1357\n",
       " 1     263\n",
       "-8      11\n",
       "Name: neighbor_familiarity, dtype: int64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['neighbor_familiarity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:,'neighbor_familiarity'].replace(-8,1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 公共服务信息 public_service_1-9     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.904085</td>\n",
       "      <td>68.229075</td>\n",
       "      <td>62.885667</td>\n",
       "      <td>66.418490</td>\n",
       "      <td>63.172456</td>\n",
       "      <td>67.187546</td>\n",
       "      <td>66.092451</td>\n",
       "      <td>65.786743</td>\n",
       "      <td>67.379650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.118684</td>\n",
       "      <td>20.546772</td>\n",
       "      <td>24.765324</td>\n",
       "      <td>22.061952</td>\n",
       "      <td>23.276158</td>\n",
       "      <td>21.524583</td>\n",
       "      <td>23.092778</td>\n",
       "      <td>23.720290</td>\n",
       "      <td>22.303287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       public_service_1  public_service_2  public_service_3  public_service_4  \\\n",
       "count      10968.000000      10968.000000      10968.000000      10968.000000   \n",
       "mean          70.904085         68.229075         62.885667         66.418490   \n",
       "std           21.118684         20.546772         24.765324         22.061952   \n",
       "min           -3.000000         -3.000000         -3.000000         -3.000000   \n",
       "25%           60.000000         60.000000         50.000000         60.000000   \n",
       "50%           80.000000         70.000000         70.000000         70.000000   \n",
       "75%           80.000000         80.000000         80.000000         80.000000   \n",
       "max          100.000000        100.000000        100.000000        100.000000   \n",
       "\n",
       "       public_service_5  public_service_6  public_service_7  public_service_8  \\\n",
       "count      10968.000000      10968.000000      10968.000000      10968.000000   \n",
       "mean          63.172456         67.187546         66.092451         65.786743   \n",
       "std           23.276158         21.524583         23.092778         23.720290   \n",
       "min           -3.000000         -3.000000         -3.000000         -3.000000   \n",
       "25%           60.000000         60.000000         60.000000         60.000000   \n",
       "50%           70.000000         70.000000         70.000000         70.000000   \n",
       "75%           80.000000         80.000000         80.000000         80.000000   \n",
       "max          100.000000        100.000000        100.000000        100.000000   \n",
       "\n",
       "       public_service_9  \n",
       "count      10968.000000  \n",
       "mean          67.379650  \n",
       "std           22.303287  \n",
       "min           -3.000000  \n",
       "25%           60.000000  \n",
       "50%           70.000000  \n",
       "75%           80.000000  \n",
       "max          100.000000  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['public_service_'+str(i) for i in range(1,10)]\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2    289\n",
       "-3     29\n",
       "Name: public_service_1, dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['public_service_1']<0]['public_service_1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对公共服务的打分，负值统一用0替代\n",
    "for col in cols:\n",
    "    data.loc[:, col] = data.loc[:,col].map(lambda x: 0 if x<0 else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### survey_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    6470\n",
       "2    4498\n",
       "Name: survey_type, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['survey_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "调查有两种类型，比例差别不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4756\n",
       "2    3244\n",
       "Name: survey_type, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['survey_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='happiness', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='happiness', hue='survey_type', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='happiness'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[train['survey_type']==1]['happiness'], label='survey_type1', kde_kws={'bw':0.1,'shade':True})\n",
    "sns.distplot(train[train['survey_type']==2]['happiness'], label='survey_type2', kde_kws={'bw':0.1})\n",
    "# 不同的调查类型的幸福值没什么区别"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## city和county \n",
    "城市和县应该是数字化后的类别数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     547\n",
       "1     502\n",
       "32    336\n",
       "64    302\n",
       "87    296\n",
       "     ... \n",
       "29     89\n",
       "11     87\n",
       "25     86\n",
       "15     84\n",
       "75     57\n",
       "Name: city, Length: 85, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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66tTXN7fd76m/ubY8uH45//QHNrV74JPPX3Mme743vbP9MraTHuMyNnb0wPe/sKnd+Q976Zoz2dGD3ve65rYfOu63ZjFvWCHmmSvnxO7lmLPPa2r3wRMevOZMdi8nnPM3zW3PPv5ua8xk4/zOuV9vbvuaY//DGjOB65dvvfZvm9v+zG/fvot53RfbY37rjivntLu46g0fb2q33zN/Zc2ZbD0bXjCqtV6TJKWU7YtulOQbs8fXJNl/wbLh85yW5LQkOeSQQ+pG57m7+uYpz29ue8unv3yNmbDIJ9/6oOa2R/7Gh9aYCWy83z7n6Oa2rz3+w2vMBDbeg885s6ndecc/fs2ZXD889OwLmtp94IQH/Ojxw86+sCnm/ScctVM5sTleeu4/Nbd94bEHJElef+63mmN+89ifWTmn3cWH3/OdpnZHP/IW16mfT73j6ua2hz92207389m3XtXc9q6/sd+PHn/x1G83xdzxqTt87NySvvV/XNnc9md+98A1ZsIiV72x/Qvi/Z7R/sXzRrnqlPc2tdvv6Q9f+vtdcdPrHyTZJ8n3k9x49vPYsi3j6lNPbWq37alPvU79fPvNf9Dcdv+nPe869bWrfOWND21qd7tnfOBHj794yjFNMXd8+gd3Kqft/uot7d9q/tJT2r4pZfO97cz7N7d90uM/usZMxr3+T9veIH7z0dftrKSX/VlbPy94xO5x9tMJH2gvTJ390D23MPXAc1/W3Pb8Y1+wxkx2Pw8+5/Smducd/+Tr1s/Z72xue94Jj5nFvHuFmEetnNN2Dzn7nOa2f37C8XNx72+MediKGe3Zjj/nsua25xx/6HXq6+Hn/Pemdu89/k7XqR92rQ++t634c8zDr1vx5xPvai/+3PfXd77489dntBd/7vHE/aYb7aa++apvNrW75cm3XHMm7I6uetP7m9vud9LDNrz/XVEw+kySw5OcneTgJJctWAZbzqdOay8yHX6iItO89/xJWwHjkU/YPQoY7B4e8IG2D9cXPLT9A/uYB77/d5ranf+w11ynfvZED3rfKU3tPnTc09ecCSx27Dmfam577vGHrzGTzfX497Wd5XDmcc5wADbet//o0qZ2+z/7sGtj/vjitphn3Xuncrq+2RUFozOTnF9KuVeS/5Tkr9JdjjZcthZXn3pGU7ttT33iulIAGr2jsciUJI9VaAIAYBe68g/bLoU88Dl77mWQXL+srWBUaz1i9v+VpZSj0p1R9KJa678nGVsG7AIfeNsDphsleeiT2u4tAQAAwJ5nV5xhlFrrPyU5a2oZAHueE89tvx/RacfuufcjAgCA3ckuKRjBRvvsqQ9panfXp/75mjOBjffqd7ddmvd7j3JZHgAAsB4KRrAFfPT0Bza3vf+Tz19jJgAAAJD82GYnAAAAAMDWomAEAAAAQI+CEQAAAAA9CkYAAAAA9CgYAQAAANCjYAQAAABAj4IRAAAAAD0KRgAAAAD0KBgBAAAA0KNgBAAAAECPghEAAAAAPQpGAAAAAPTsvdkJAAAAwK70lTd+u6nd7Z6x/5ozga3LGUYAAAAA9CgYAQAAANCjYAQAAABAj4IRAAAAAD0KRgAAAAD0KBgBAAAA0KNgBAAAAECPghEAAAAAPQpGAAAAAPQoGAEAAADQo2AEAAAAQI+CEQAAAAA9CkYAAAAA9CgYAQAAANCjYAQAAABAj4IRAAAAAD0KRgAAAAD0KBgBAAAA0KNgBAAAAECPghEAAAAAPQpGAAAAAPQoGAEAAADQo2AEAAAAQM+mFoxKKaeXUi4tpbxgM/MAAAAA4FqbVjAqpRyXZK9a62FJDiil3G6zcgEAAADgWpt5htERSc6aPf5EksM3LxUAAAAAtiu11s3puJTTk/xxrfVzpZT7J7lbrfWVc78/McmJsx/vkOTLC57qFkm+s2L3e1rMruxLjG20p8bsyr7E7Nq+xNhGe2rMruxLjG20O8Tsyr7E2EZ7asyu7EvMru1rUcyBtdZtoxG11k35l+SPkhw6e3xckt/fyee5/Poes9XzE7P18xOz9fPb02K2en5itn5+YrZ+fmK2fn57WsxWz0/M1s9PzNbPb0+L2er5beYlaZ/JtZehHZzkq5uXCgAAAADb7b2Jfb8/ySWllAOSPCDJoZuYCwAAAAAzm3aGUa31mnQ3vr4syZG11u/v5FOdJmaX9iXGNtpTY3ZlX2J2bV9ibKM9NWZX9iXGNtodYnZlX2Jsoz01Zlf2JWbX9rVyzKbd9BoAAACArWkz72G02yil3LyUclQp5RabnQtbQynllqWU+5VSbrLZubC5zA9bz85sk42IaX2OzcpvK8XsTvb018dqSik3KqX8SinlP2x2Loyzz8KezT6+a+3WBaNSyv6llEtWaH/TUsoFpZQLSynnllJu0BBzyyQfSnKPJJ8spYz/ubnF+X22se3epZSvlVIumv270wr9nFJKecgK7Z82188VpZS3NMTsW0o5v5RySSnl1MZ+bltK+dAs5rWt+a1iOAZKKb9QSvlAa0wp5Taz9fCJUspppZTSEHPnJH+W5JeT/LdF42hsfJZSfrGU8tEV8rtVKeXrc9trdPwt6OuDpZS7Nvbzkrk+vlRKeV5DzM+WUj5eSvnLUsoLG/u5WynlY6WUS0spv71sPaxibN+emh9GYibnh5E2B2Ziflj0vMvmhwWvZ+n8sKSfpfPDSNyzp+aHkZj9p+aHkZg7tMwPZcWDgjIyZzeMhWHMgQ1jYRhz62G/rfnNli8bD2OvaWo8LOpn4XgYiXlhw1gYxty9ZbvurPn1NLVdhzEt+/hITPMxwHAbLtumC/pqPg4Y6WvyOGCun+ZjgLmYfaf28ZGY265jLIytp1LKDZet6wUx7ymlfKR07//7NsYdnOTDSe6Z5M9LKXds6Wu2fJ9Syt+vkN9n534+aoV+fq+U8szGfub38ctKKR9piLlPKeW9s8dvL6X8+KL1Pov/0dhcYZ89pZTykBX32e0xq+yzvf2mZZ+d62eV/XXYT8v+ur2flY7Z5+JW2We3x6xrn93hNUyNhZGYM6bGwqJ1tWy7jsSc3rJdl/S17D12GPOvU9t2wbqbOt4axnxkTdt1hzHWsF17MS37+EjM5D6+aPxPjIVhP037+JK+lo2FYV+T+/lIzGi/EzG33amxsDN/9m0r/Euyb7o37b9ZIebpSY6aPX5zkmMaYu6X5NDZ49ck+dUV+ntHki81tr1bklftxHq4V5L3XYf1+IYk/7mh3bOSPHr2+F1JDmmIOWtu3f1ZkiMm2u+f5JLZ4x9Pcl6SS5M8qWUMJPmPs5iLWsdNkpcn+YXZ4wuS3Lkh5pFJ/uPs8dlJ7tAyPpOUJB9dMb/jkjxtYr2N9fXrSV6/Sszc796b5FYNub0uyWGzx59Ksq0h5i+S3Hq2Li5NctuRmJvOtsWFSc5NcoMkp8/av2DB6xnu249f9PqWxDwrE/PDSMwxmZgfxmJmjxfODyMxL8rE/LAgt8n5YVF+s59H54cF627p/DAS839lYn5Icsskf5nk+Un+e5JtDWNhOGc/omEsDGOe3TAWdnhvmBoLi+IaxsMw5uSG8TCW39LxsCi3ibEwjPnu1Hadi90/yWdnj5du17mYdyT5UlY4BpiLaT4GmItpPgYYbsNl23RBX83HAfPPPbVdl+WzaLuO5NZ8DDAXM3kMkO6Pr3wtyUWzf3dK8pIkn07yxgXP31tPSfZK997/1SU5DWOOSfLE2eOTkzyrMe7gJA+ePX5GkqdMxcwtf3mSHzb289NJ3jOxPXfoJ8nPpXs/3qs1Zu53v5vkuIbcXp3kkbPHr0ryhCU5/mhspnGfHcQ07bODmKZ9dmy/GdtHlvTTtL8O+xnrtyW32fKp/XU+v6Z9dhDTss8+Ldfur1ckeUsa5+/519AyFgYxz28ZC2Pramq7juS20uexubjmz2TDbTm1befavLhluw5iLmzYrvsmOT/JJUlOnS2bOt4ajrG7T23XkZgXTW3XkZjfyfSx9+j4XzYWRmLu0TIWxvqaGguL8ls2Fhasu6lj72HMJ6bGwti/3fkMo39P90HgmtaAWusptdYLZz9uS3JVQ8zHaq2XlVLunW7g/GVLX6WU+yb5lyTfakzv0CTHllI+VUp5Vyll8i/Yle5bnf+a5KullIc29jMff6sk+9daP9PQ/J+T3KGUcrN0H/i/1hBz+yR/M3t8VbpCwKJc9k1yZpIbzRY9M8nltdbDkjy4jF/6NRwD/3eS4ydy6sXUWp9fa/0fs9/9dJLvNMS8J8mVpZQHpZtg/64htyR5YpJPrpJfunHx9NKdxfOHLTGllJsneW2S75ZSjmzsJ7PYuyf5Rq31Gw0x/5zkF0op+6cr6nyvIebmtdZ/rN1M9c9Jfmok5teTvK7WelS6/eeR6Q5+D0tyQCnldsOAkX37H8Ze30TMX0/ND2NzyNT8MBYzNT+MxPy/mZgfRmK+m4b5YdG8uGx+GIm5SSbmh5GYG2Z6frhjkufUWl+e5CNJ7pvpsTCcsy/I9FgYxpzRMBZ2eG9oea8Yi2sYD8OYH2Z6PAxjLsvEeFj0fjcxFoYx303jvJ/uQG+fUspxmdiuszzm11PTMcB8TOsxwCCm6RhguA1bjwEG7ZqOA+ZjWo8DxvKZOgYYxDQdAwxiWo4B7pzk3bXWI2qtRyT5iSSHp1vXXy+l3G8kpree0hWdTkzy1QUvfyzm/FrrGbPfLTseHMZ9sdZ6XunO3j023ZdAS2Nm31L/fLpi2F819vPLSQ6bfQv8oVLK2Hvl2Hh5S5K/TfKoUspejTEppeyT5P611vc15Paf0rCPj4zNyX12GNOyz47ETO6zY/vN1D47EjO5vw5jWvbXRW0a9tdh3OQ+OxIzuc/WWt88t79ekm68Tc7f868hyVfS+Blu7nW/vPUz3Py6WmEu3p7bL2WFz2NzcZ9P42ey4bac2raDfv4ujZ/H5mK2ZXqffWySd9Za75XkJqWU38v0dh2OsW9nersOY05t2K7DmLc3vC/vMP4bxsIw5ui0jYVh3D9meiyM7p8TY2EYc+XYc0zE/Ezaj9F+ZLctGNVar6k7+ZfVSin3TLJvrfWyxvYl3Q7wb+ne8Kba3yBd1e+5K6T16ST3qbUenu6D9wMbYh6X5P9M923PPcqCU5CXOCldNbfFp5LcLl2l8kvpPhRMOTvJi0t3Ot7RST6+pO3wQOKIdN90JF11+5BhwHAM1FqvqrX+67KEFo2bUsoj0h0I/lNjzI2T/Fq6syTqVEwp5aeTPCbdB6RV8rsg3Vk890xy+9JdDjcV85x0Zwm9JcnjSinHNL6mpDuz4g2NuX04yb3TjYlPpitqTMX8RSnlGaWURyc5KN0b7DBmeID4mFw7Fj6R7sPEqLl9++LW+WE4H7TMD/NtWueH7THpJuqm+WEu5sI0zg9zMbfPCvPDyOuenB/m+npnGueHuZiXZWJ+GDnw/9U0jIXhNmkZC2PbcWosDGNWGAvz7UoaxsMg5nNpGA+DmMekYTwseA1Lx8Ig5sw0zPuDA7cjMrFdh++rLccAi96Ll23XsZip7TqMaT0GGGk3eRwwEjN5HLAkn4XbdSRm8hhgJKblGGBYjLhvknNmXyp8LN23tEPD9fSrY+/dEzEPnOX8s9v7XCUuyUPSfcH1w8aY7WcutuZ3UJJfmX14uyjJExpiHpSuEP+idMcor17h9Tw2yZ825vbeJM8t3WVyT06y6DYAvbGZ5PENc/HoeJ6Yi3eIaZiLhzHPzvQ+O3w9P53peXgY8/Sx19eyDjL9njzs6+cy/b48jDknjcftc8WIA9N4jLb9Naz4Ga73uhs/w52U5M0rfh7b3s+qn8e2x63ymWy4LVs+j21vs8rnse0xLXPxsLBwUKa36zCXbzZs19H8J7brDjEN+/gw5vuZHgvDmAvSNhaGcQ/L9FhYtB2XjYVhzEULnmNZzLvT/tn8WnWFU+624r8subxnQfubJ7k8yYE70ddLkzyiod2Lkjx8lfyS/MTc42cm+e2GmDcmOXr2+BeywqVp6YqFf7lC+3cl+anZ499KcmJj3OHpDiYmT1OdX1+zAXzT2eMTMzv9uWUMtKzz+TZJfjbdG8RNVx1r6U5t/KWG1/Nft7dbJb/BuHhtkuMbYs5L8vOzxw9Id7ZOy3q4WZKPrpDbB5Mf/aXFP0737eRUzF7pThe/NMljJvq552wcnJ7k4Nmy+yd57oL2O+zbU+t6GNMyPyxqs2x+mI9pnR8GMU3zwyCmeX4YWQ+T88Ogr6b5YaSfyfkhXUHlTbOxcEbLWBjbJi373XxMy1hYtO2XjYWRuBe3jIclfU2+X8xi/kfreBish+b3irmYpds13RmJF6Wbcy5Kwz6+aL+Z2Id2iJnarov6WbZdhzHLnmMibnI/H4mZ3M8XrIel23Wkn8l9fEE/U2Ph7kluOXv8pnT7w0NnP98+s0sjBjGj62liXe8Qk+5spouz/PK6hdskXbHk5IaYFyf53WU5jsScnGvfXx+S5A2NMU+d/bxPxi85X7TuPpnkRiusu7sleWuSty5Zd6Njc2I77RCT6X124T6QxfvsMOb/G47dhphzF42NJTGfWJTrxDpoeU/eIb9M77NjfTUdtyd5RZIj036MtsNrWDYWxmKmxsIwJu1z8XxM8+exQVzTMdfIa2rZtvP9tB5vDfuZmosPTFc0flG6P7ve8r48msvEut4hZmq7LnvNWbyPD2Mmx8JIzFPmfrfs2HsY97+mxsKC9TD1vjyM+ebUWFjQz0qfzWvdvS9JW9ms0nxWkufVWq9sjDm5lPK42Y83y/hlN0P3S3JSKeWiJHcppby1IeYdpZSDS3cK8bHpvkGe8nfpih1JdwZO02uauVcWnxo95oZJ7jTL75cyclbNAlckuU26+92s4gfpDniS7puytYzV0l0K9+5090lqPSPlzbMzHpL2MXGfJK+aGxMva0zxI6X7i2w3THd2xRcaYnZ2XDw03fXLrQ5IcutSyk+mO3icHBO11n9P8uXZj+9a1K50l9W9IcmT0jAWdnLf7sW0PMdIzOT8MPK8k/PDSMzk/DAS0zQOFrzupfPDSMzk/LCgnysyMT/UzknpioyHZnosrDxnL4iZGgvDmINa+h2JOzrT42EYc2rDeBjG/EkmxsOC9TA1FsZirsjy7frcJG+qtX5v9nPLfL8z76vDmLdlep4YxtSG7dqLSXc2SEuuw7gLGo4DhjFPzfR+Prbupo4Bhv0ckOljgLF+rsjysfD5Wus3Z4+/lLaxsDPHS2MxZ6S7/PTyFeK+Xq79Iw83y/h4GMY8Jckxc+vlvIaYR6YrFCXJwxe8xmHM32d6LOywHkopByX5fq31XxrXwefSbddfTPchbJGdORYZi5naZ4cxxzbss8OYz2V6nx3GpGEcDmOOHHl9UzFXpu2YfYf8Mr3PjvV1RSbel0spP5bkyFrrJ9N+vL7q545ezArHefP9tL5vzMesMr/Mx7WO9+F6aFkv821aP48Nn/eKLN+ur0hXcP4v6ebiR2d6u+7MZ8NhTMn0dh3G7Nuwjw9jnpbpsTCMeWXjWBjGvTjTY2Fs3U2NhWHMT448R0s/V2TVz+atlaWt+i8rnGGUbrB8N9ferK3lG+Dtl4NcnOSUzL7x2ej80r3xfj7djV1f3hhzk3SnBl+c7vrNHW5UvCT2FRm5ueGS9vdI8sV0bwwXJrlxY9xLkjx21fWV7iDkhNnjMzO7uXLLOm5Z53P9vCpdhXb7mLhPQ8xt053id0mSF666/VfM78h0E/fnkzyjMeaAdIWfv5htq5u05Jfum4W7rZDbg5L8z3T3jnp3Ftxkc6SfM5Pca0nbG6S7FGH7TfAel+R35sbTo0diRvftZet6JObFY88x1U8m5odFuS3Lb0FuS+eHkZjHp2F+WPCals4PIzEnZ2J+WNDP0vlh9ryPmz1+w+w1TY2F0Tl7YiwMYybfK0Zimt4rlrVbMh6GMXdqGA/DmMn3i7HcGsbCWMzUdr14bt1+L908snS7LplPFm7XYUzLdh2JWekYYJjPivmtdBwwi1npOGB7PlPbdaSflY4B5vqZGgtnpbuR9F7pzr54UWY3u05337/fH4kZXU/L1vUwJt3Ztz+cGwvPboy7QbrLdi5O8p4kP9ma37IcR/q5ZboPDl9Id4byjzfE7DVre3GSzya5U0tu6b5x/q3WdTe3baaOf0bH5sR2GsY8P9Nz8Q79ZPp9eeF+s2QbDWPus2g7L4k5cFG/E69ncn9dkN/U+/JYX5PH7bPnfv3s8eQx2ux3O7yGZWNhGJPG+XvRupoYd/P9NM/Dg7imuXiYX+O2ne+naS4e6WdqLj433b3T9kp3M+QXT23XRblMrOthzDOntutIzK0zvY8vXE+L8huJaRoLI3Etx1s75Dc1FkZi7j01Fhb0s9Jn81rrjw6kYUsopVxUaz2idH+u/Px0hYPD0t3RffL+Uez+SilPSzdpbq/kn5HuNMqPpzvIP7Tu5P3L2L3MzgA8K92lI19I8rx0b8DGwh5i9m3fMekK8Lbr9Uwp5RfTfVlR0l3m/MJ0Y+HydGffHV1r/YfNyxAYKqW8It0fpnlf6W7Ibv7ezZVS7pHuePvAdEWO42O7MqNgxJZVSjkg3XWWHzFJXb/NCgdHJbm41tr6lwfZAxkLeybble1K9xe7HpTuHjz/c7PzAZYzf++ZbFe2UzACAAAAoOd6ddNrAAAAAKYpGAEAAADQo2AEAHAdlVJeP/j5LqWUu2xONgAA1517GAEAbLBSyhOSpNb6J5ubCQDAznGGEQBAo1LKT5ZS3lNK+VQp5bxSyg1nyy+aa/MHSZ6b5LmllI/Plr2klPKo2eMXl1IeuQnpAwA0UzACAGh3YpLP1VoPT3JOkl8cNqi1Pi/JK5O8stb6K7PFb0/yqNnjo5O8f/2pAgDsvL03OwEAgN3Iz6crFCXJn7QG1Vr/vpRyk1LKEUm+UGv93xufGgDAxnGGEQBAuy8lufvs8e8n+Y0F7X6YZPvlamW27D1J3pbubCMAgC1NwQgAoN1pSe42u2fR3ZK8Y0G7C5McV0r5iyT3mi07O0lN8ql1JwkAcF35K2kAAGtWSrljkjOSvKXWevpm5wMAMEXBCAAAAIAel6QBAAAA0KNgBAAAAECPghEAAAAAPQpGAAAAAPQoGAEAAADQo2AEAAAAQM//D9RXeFULktOyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "sns.countplot(x='city', data=data,ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44     108\n",
       "72     108\n",
       "102    102\n",
       "32     102\n",
       "70     102\n",
       "      ... \n",
       "36      39\n",
       "37      38\n",
       "35      38\n",
       "40      37\n",
       "81      23\n",
       "Name: county, Length: 130, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['county'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25,5))\n",
    "sns.countplot(x='county', data=data,ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e5fe9f9a08>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train.loc[:, ['survey_type', 'happiness', 'city', 'county']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "city和county,城市、县和幸福值调查类型之间没什么关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gender、birth、nationality、religion、religion_freq "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    5834\n",
       "1    5134\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gender性别\n",
    "data['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 4.0    4818\n",
       " 5.0    1410\n",
       " 3.0    1159\n",
       " 2.0     497\n",
       " 1.0     104\n",
       "-8.0      12\n",
       "Name: happiness, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['happiness'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 异常值-8填充为众数4\n",
    "data.loc[:,'happiness'].replace(-8,4, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAECCAYAAAD+VKAWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAU9UlEQVR4nO3dfZBU9Z3v8fd3CV4UMBIE1BCi92qpuwb0OroggwwpDD6sqTKhfNbENSE+rFXZbLa8lpuEuEvUe1djYjRK8CKYrGLict31qimMDmCMIiQ+xEoocrOg4xoyQQSfCGJ97x9z1AGnsSf26TPMvF9VXXP6e7p//e2qYT78zu/06chMJEn6s6obkCT1DQaCJAkwECRJBQNBkgQYCJKkwgeqbuBPtffee+f+++9fdRuStEtZtWrVHzJzVE/7dtlA2H///Vm5cmXVbUjSLiUi1tXa5yEjSRJgIEiSCgaCJAnYhdcQevLGG2/Q0dHBli1bqm6lYYYMGcLYsWMZPHhw1a1I6uf6VSB0dHQwfPhw9t9/fyKi6nbet8xkw4YNdHR0cMABB1TdjqR+rl8dMtqyZQsjR47sF2EAEBGMHDmyX814JPVd/SoQgH4TBm/pb+9HUt/V7wJBkvSnaXggRMQHI+K+iFgSEYsjYreIeDYi2ovbx4rHfT0iHo+I73R77rtq79fs2bNpb29v1HAAfPGLX2zoeJLUF5SxqHwWcG1mLomI7wL/A7g9My996wER0QK0AkcDl0bEdOClHWuZ+UAJ/b1v1113XdUtSGqA7/zdv5cy7t9cc3Ip45at4TOEzLwxM5cUd0cB24BTIuLhiPhBRHwAOBa4K7u+ru0BYEqN2nYiYlZErIyIlZ2dnXX3tGTJEqZOncrhhx9OR0cHJ554Ih//+Mc577zzgK5ZxAknnMDUqVOZOXMm27Zt49Zbb2XatGlMmzaNGTNmsGnTprfHa2tre3u7vb2d888/n+OPP55DDz2Un/zkJwB87Wtfo7W1dbvnXnrppbS2tjJ58mTWrVtXsyZJVShtDSEiJgEjgCXA1MxspWsWcCIwFHi+eOhmYEyN2nYyc25mtmRmy6hRPV6bqUe/+c1vWLp0KWeeeSa33HILF198Mffddx9r165l/fr1AEyZMoWlS5cyZswY7r77bgAOOuggHnroIaZPn868efNqjr906VJ++MMfsmDBAhYtWsQTTzzBsmXLePjhhznppJNYsGABAHfccQft7e3cfPPNvPzyyzVrklSFUj6HEBEfAq4HPg38LjP/WOz6NXAQ8Aqwe1EbRlcw9VRriHPPPReA0aNHAzBv3jzmz5/Piy++yOuvvw7AkUceCcD48eNZu3YtI0eO3K52zz331Bz/lFNOYfjw4YwePZqtW7eyevVqfvvb39LW1saWLVuYMWMGAHPmzOHkk09m2LBhXHPNNTVrklSFMhaVdwPuBC7LzHXAbRExISIGAacATwKr6FovAJgArK1Ra4ihQ4e+vf2Vr3yFmTNncvvtt29XX7FiBQC/+MUvOPDAA2vW3mt8gIMPPpi2tjba29uZN28eRx99NK+99hqdnZ3cd999HHfccXzve9/rsSZJVSljhnA+cCRweURcDjwE3AYE8G+Z+UBE/BlwZUR8Czi+uK3rodZwn//857nyyiu56aabAHj++a6jVI8//jhtbW3ss88+nHzyySxcuJDnnnuOadOmMWTIEO688866X+Pwww/nIx/5CFOnTmXr1q3cfPPN7LHHHqxZs4bJkyezZcsW5s6d22NNkqoSXWu4FbxwxO7AScDPM/O3tWq1tLS05I7fh/CrX/2KQw89tNe9zJ49m7a2tu0Wi2+99VYAPvvZz/Z6vEb7U9+XpJ0biGcZRcSqzGzpaV9l1zLKzNeBH71XrRlmz579rlpfCAJJaiY/qSxJAgwESVLBQJAkAf3s+xDey5F/v7Ch4636X+c2dDxJqpIzhCbauHEjJ554IlOmTOGCCy6ouh1J2o6B0ES33XYbZ599NsuXL+fll19mx9NmJalKBkITjRw5ktWrV/PSSy/x3HPPMW7cuKpbkqS3Dag1hGb7whe+wOrVq9++P23aNNasWcO3v/1tDjnkEEaMGFFhd5K0PQOhRDfffPN298866yxuuukm9txzT6699lrmz5/PrFmzKupOkrZnIDTRa6+9xtNPP83EiRN57LHHmD59etUtla6sSwNA3748gLQrGlCBUPVpopdddhnnnXce69atY9KkSZxxxhmV9iNJ3Q2oQKja0UcfzTPPPFN1G5LUI88ykiQBBoIkqWAgSJIAA0GSVBhQi8rPXvGxho437qtPN3Q8SaqSM4QmWL9+PVOmTKm6DUnaKQOhZBs3buQzn/kMr776atWtSNJOGQglGzRoEIsWLWLPPfesuhVJ2qkBtYZQBYNA0q7CGYIkCTAQJEmFAXXIyNNEJak2ZwhN0t7eXnULkrRTBoIkCTAQJEkFA0GSBBgIkqTCgDrLaPL1kxs63k8v+WlDx5OkKg2oQGi2TZs2cfrpp7Nt2zaGDRvGokWL2G233apuS5J65CGjEv3gBz/gS1/6EkuWLGGfffbh/vvvr7olSaqp4TOEiPggcEcx9ivAacB3gUOBezPzn4rH3VJPbVd20UUXvb3d2dnJ6NGjK+xGknaujBnCWcC1mXkc8DvgdGBQZh4D7BcRB0XEp+qpldBbJX72s5+xceNGJk6cWHUrklRTw2cImXljt7ujgLOB64r7DwKtwBHAnXXU1nQfOyJmAbMAxo0b1+jWS/Hiiy9yySWXcNddd1XdiiTtVGlrCBExCRgBPAc8X5Q3A2OAoXXWtpOZczOzJTNbRo0aVVbrDbN161ZOPfVUrrzySj760Y9W3Y4k7VQpZxlFxIeA64FPA18Cdi92DaMrhF6ps9ZQzT5N9JZbbmHVqlXMmTOHOXPmcOGFF3Laaac1tQdJqlcZi8q70XXo57LMXBcRq+g6/PMoMAFYDXTUWdulXXjhhVx44YVVtyFJdSljhnA+cCRweURcDswHzomI/YATgIlAAsvrqEmSmqThh2Uy87uZOSIz24rbAqCNrv/5T8vMTZm5uZ5ao3uTJNXWlE8qZ+ZG3jmDqFc1SVJz+EllSRIwwK5ltPTYqQ0db+qype/5mBdffJFVq1ZxxBFHsPfeezf09SWpkZwhlOiFF17gpJNOYsWKFUybNo3Ozs6qW5KkmgbUDKHZnnnmGb75zW8yceJENm7cyM9//nNmzJhRdVuS1CNnCCWaPn06EydOZNmyZaxYsYJJkyZV3ZIk1WQglCwzWbRoEYMHD2bQoEFVtyNJNRkIJYsIbrjhBo455hjuueeeqtuRpJoMhBJdffXVLFy4EICXXnqJvfbaq9qGJGknBtSicj2niTbSrFmzOPXUU5k3bx6HHXYYn/jEJ5r6+pLUGwMqEJptxIgRLFmypOo2JKkuHjKSJAEGgiSpYCBIkgADQZJUGFCLyt/5u39v6Hh/c83JDR1PkqrkDKEJ1q9fzxFHHFF1G5K0UwZCE3z5y1/m9ddfr7oNSdopA6FkDz74IEOHDmWfffapuhVJ2ikDoURbt27liiuu4Kqrrqq6FUl6TwZCia666iouvvhir2EkaZdgIJTogQce4IYbbqCtrY0nnniCz33uc1W3JEk1DajTTpt9muiyZcve3m5ra2PevHlNfX1J6g1nCE3S3t5edQuStFMGgiQJMBAkSQUDQZIEGAiSpMKAOstoztkzGzre5d//UUPHk6QqOUMo0bZt2xg3bhxtbW20tbXx9NNPV92SJNU0oGYIzfbUU09xxhlncPXVV1fdiiS9J2cIJXr00UdZvHgxra2tnHXWWWzbtq3qliSpJgOhREcddRRLly7l4YcfZq+99uLee++tuiVJqqmUQIiIMRGxvNj+cER0RER7cRtV1G+JiEci4h+6Pe9dtV3Z+PHj2XfffQE45JBDWLNmTcUdSVJtDQ+EiBgBLACGFqW/BOZkZltx64yITwGDMvMYYL+IOKinWqN7a7ZzzjmHJ598kjfffJPFixczYcKEqluSpJrKWFR+EzgNuLu4PxE4ISLOBR7NzL8F2oA7i/0PAq3AET3UtvsvdUTMAmYBjBs3rteNNfs00a9+9auceeaZZCaf/OQnmT59elNfX5J6o+GBkJmbASLirdJ9wD9m5ssR8X8jYjxds4fni/2bgQNr1HYcey4wF6ClpSUb3XujHXbYYTz11FNVtyFJdWnGaaePZOYfi+1fAwcBrwC7F7VhdB266qkmSWqSZvzR/XFE7BsRewAzgF8Cq+g6JAQwAVhbo9ZrmX1+4tAr/e39SOq7mjFD+DrwELAVuCkzV0fEC8DyiNgPOIGudYbsodYrQ4YMYcOGDYwcObL7IatdVmayYcMGhgwZUnUrkgaAPykQIqI1Mx/e2WMys634+RBwyA77NkdEG3Ac8D8zc1Mx7rtqvTF27Fg6Ojro7Ozs7VP7rCFDhjB27Niq25A0ANQVCBGxJDOP61a6Epjyfl44MzfyzllFNWu9MXjwYA444ID305YkDVg7DYTijKAjgA8Xp41C19lAW8puTJLUXO+1qBw9/NwAnFpaR5KkSux0hpCZTwJPRsTBmbmwST1JkipQ76LydRFxOrDbWwUDQpL6l3o/h3A/MJauQ0Zv3SRJ/Ui9M4TNmfnPpXYiSapUvYHwcETcDiwEXgXIzGWldSVJarp6A+ENuq5DdBRdh4sSMBAkqR+pNxDW0hUCb4WBJKmf6c3F7YKuq5F+Cji2nHYkSVWpa4aQmQu63b0pIm4sqR9JUkXqvZZR9xnBnsBflNOOJKkq9a4hTOOdtYOtwEXltCNJqkq9awjfANYDHwL+AKwurSNJUiXqDYT/DYym6/uRPwzML60jSVIl6j1k9JHMPKfY/nFELC2rIUlSNeoNhP+MiMuAx4BJwPPltSRJqkK9h4wuoCs8ZgKbgS+U1pEkqRL1BsL3gWcz8yJgOF1rCpKkfqTeQBjx1ofTMvMbwN7ltSRJqkK9awgdEXEpsIKuC9z9vryWJElVqHeG8FngNbrWEF4Hzi2rIUlSNeq9ltEfgetL7kWSVKHeXO1UktSPGQiSJMBAkCQVDARJEmAgSJIKBoIkCTAQJEkFA0GSBBgIkqRCKYEQEWMiYnmxPTgi7omIRyLir3tTkyQ1T8MDISJGAAuAoUXpEmBlZh4D/FVEDO9FTZLUJGXMEN4ETqPri3QA2oA7i+1HgJZe1LYTEbMiYmVErOzs7CyhdUkauBoeCJm5OTM3dSsN5Z2v3NwMjOlFbcex52ZmS2a2jBo1qtGtS9KA1oxF5VeA3YvtYcVr1luTJDVJM/7orgJai+0JwNpe1CRJTVLvN6a9HwuAeyNiCvDnwGN0HRqqpyZJapLSZgiZ2Vb8XAccB/wUmJ6Zb9ZbK6s3SdK7NWOGQGb+J++cQdSrmiSpOVy4lSQBBoIkqWAgSJIAA0GSVDAQJEmAgSBJKhgIkiTAQJAkFQwESRLQpE8qS9JAMufsmaWMe/n3f1TKuG9xhiBJAgwESVLBQJAkAQaCJKlgIEiSAANBklQwECRJgIEgSSoYCJIkwECQJBUMBEkSYCBIkgoGgiQJMBAkSQUDQZIEGAiSpIJfkKNd1q76JSRSX2UgCIClx04tZ+CjvlzOuJIazkNGkiTAGYKkPq602Ss4g92BMwRJEmAgSJIKpQdCRHwgIp6NiPbi9rGI+HpEPB4R3+n2uHfVJEnN04wZwnjg9sxsy8w24L8ArcDRQEdETI+Ilh1rTehLktRNMxaVJwKnRMRkYB3wJHBXZmZEPACcDGzqofZAE3qTJBWaMUN4HJiama3AS8DuwPPFvs3AGGBoD7V3iYhZEbEyIlZ2dnaW2rQkDTTNCISnMvOFYvvXwCt0hQLAsKKHnmrvkplzM7MlM1tGjRpVYsuSNPA0IxBui4gJETEIOIWu2UBrsW8CsBZY1UNNktREzVhDuAL4FyCAfwP+CVgeEd8Cji9u64Ard6hJkpqo9EDIzF/SdabR24qziE4CvpWZ/1GrJklqnkouXZGZrwM/eq+aJKl5/KSyJAkwECRJBQNBkgQYCJKkgoEgSQIMBElSwUCQJAEGgiSpYCBIkgADQZJUMBAkSYCBIEkqVHJxO0n9z+TrJ5cy7jf8M9U0zhAkSYCBIEkqGAiSJMBAkCQVXK3ZxbhwJ6kszhAkSYCBIEkqGAiSJMBAkCQVDARJEmAgSJIKBoIkCTAQJEkFA0GSBBgIkqSCgSBJAgwESVLBK5pJA8yzV3ysnIFH7FnOuGoaA6EEpf2DA//RSSqNh4wkSUAfDISIuCUiHomIf6i6F0kaSPrUIaOI+BQwKDOPiYgbI+KgzFxT1usd+fcLSxl38fBShtUAUtbvJvj7qdoiM6vu4W0R8W3g/sy8NyJmAsMzc363/bOAWcXdg4HVFbTZX+0N/KHqJqQe+LvZWB/NzFE97ehTMwRgKPB8sb0ZOLD7zsycC8xtdlMDQUSszMyWqvuQduTvZvP0tTWEV4Ddi+1h9L3+JKnf6mt/cFcBrcX2BGBtda1I0sDS1w4Z/R9geUTsB5wATKy2nQHFQ3Hqq/zdbJI+tagMEBEjgOOAZZn5u6r7kaSBos8FgiSpGn1tDUGSVBEDQUTEmIhYXnUfUncR8cGIuC8ilkTE4ojYreqe+jsDYYAr1mwW0PUZEKkvOQu4NjOPA34HHF9xP/1eXzvLSM33JnAacHfVjUjdZeaN3e6OAn5fVS8DhYEwwGXmZoCIqLoVqUcRMQkYkZmPVt1Lf2cgSOqzIuJDwPXAp6vuZSBwDUFSn1QsIt8JXJaZ66ruZyAwECT1VecDRwKXR0R7RJxWdUP9nR9MkyQBzhAkSQUDQZIEGAiSpIKBIEkCDASpVBExOyLaqu5DqoeBIEkC/KSyVFNE7A78KzAS+H/A08B/B0YDT2fmxRExGxhM11e/fpCuC7D9EfghMAgIoD0i9gAWdn9u8RrtwOPA+Myc0bQ3J/XAGYJU2yFABzAZ+G/Aa8AvM/NYYN+IGF887sDMnAr8C/BxYBZwT2ZOA94oHjOrxnMnAj8zDNQXGAhSbc/T9UnZZcC3gIOBU4r/1f9X4MPF4xYWP38P7AYcADxV1FYWP2s995eZ+a/lvQWpfh4ykmo7HvjHzFwMEBGjgBWZOT8i/gp4FvhL4NUdnrcO+HPgQeBw4MfA6h6eC/BK6e9CqpMzBKm2XwDXR8SDEXEH8BBwQkQsAy4AnqvxvO8Bny5mA3t2q9XzXKkyXstIqiEiPg+cQdc6wBvAP2dme6VNSSUyECRJgIeMJEkFA0GSBBgIkqSCgSBJAgwESVLh/wN8WBIUW0gwPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='gender', hue='happiness', data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "性别比例差不多,不同性别的人幸福指数也差不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1965    284\n",
       "1968    273\n",
       "1970    267\n",
       "1963    262\n",
       "1955    261\n",
       "       ... \n",
       "1922      8\n",
       "1923      4\n",
       "1924      4\n",
       "1921      3\n",
       "1920      1\n",
       "Name: birth, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['birth'].value_counts()  # birth是出生年份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,20))\n",
    "sns.countplot(y='birth',hue='happiness', data=train)\n",
    "plt.title('不同出生年份的人的幸福值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    10097\n",
       " 8      378\n",
       " 4      216\n",
       " 6      136\n",
       " 3       90\n",
       " 2       24\n",
       "-8       20\n",
       " 5        6\n",
       " 7        1\n",
       "Name: nationality, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['nationality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -8是个错误值，应该是8\n",
    "data.loc[:,'nationality'].replace(-8, 8, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10097\n",
       "8      398\n",
       "4      216\n",
       "6      136\n",
       "3       90\n",
       "2       24\n",
       "5        6\n",
       "7        1\n",
       "Name: nationality, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['nationality'].value_counts()  # 调查来源绝大部分是汉族，与我国人口比例相吻合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='nationality', hue='happiness', data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    9639\n",
       " 0    1183\n",
       "-8     146\n",
       "Name: religion, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# religion\n",
    "data['religion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    9432\n",
       " 3     483\n",
       " 4     318\n",
       " 2     257\n",
       " 6     153\n",
       " 8     124\n",
       " 9      70\n",
       " 5      59\n",
       " 7      50\n",
       "-8      22\n",
       "Name: religion_freq, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['religion_freq'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -8异常值，填充为众数\n",
    "data.loc[:, 'religion_freq'].replace(-8,1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEDCAYAAAA1CHOzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbE0lEQVR4nO3deZRV5bnn8e8vCEEBA2IBGoLaLVe9UZC25IIUcHBBcLjYC5vWOCYmpiISuzPdVq+JUTs4pKPROIJwEdQoRONNYsQsiBRDHKnEsSNNbgIKV0kFGZwI4n36j7NLiqIKTuHZex+qfp+1zqp9nrP3+z5VFPupd797UERgZmb2ibwTMDOzyuCCYGZmgAuCmZklXBDMzAxwQTAzs8Q+eSewpw488MA49NBD807DzGyvUl9f/9eIqGrps722IBx66KEsX7487zTMzPYqkla39pkPGZmZGeCCYGZmCRcEMzMD9uI5BDOzcvrggw9Ys2YNW7ZsyTuVsujatSv9+/enc+fOJW/jgmBmBqxZs4YePXpw6KGHIinvdD6WiGD9+vWsWbOGww47rOTtfMjIzAzYsmULvXv33uuLAYAkevfu3ebRjguCmVmiPRSDRnvyvbggmJkZ4IJgZtaiq666irq6urK2+fWvf72s7ZWbJ5Ur0G3f+uUO779244ScMjGzcrr55pvzTmGXPEIwM2vFggULGD16NMceeyxr1qzhlFNO4cQTT+SCCy4AiqOIk08+mdGjRzNp0iS2bdvGPffcw5gxYxgzZgzjx49n06ZNH7VXKBQ+Wq6rq+PLX/4yJ510EkcddRS/+c1vAPje975HTU3NDtteeuml1NTUMGLECFavXt1q7ONyQTAza8Uf//hHFi9ezNlnn83MmTOZMmUK8+fPZ9WqVaxbtw6AkSNHsnjxYvr27cvPf/5zAAYOHMiiRYsYO3YsM2bMaLX9xYsX89Of/pTZs2czd+5cnn/+eZYsWcKyZcs49dRTmT17NgAPPvggdXV1TJs2jbfffrvV2MflQ0ZmZq04//zzAejTpw8AM2bMYNasWbz11lu8//77ABx33HEADBo0iFWrVtG7d+8dYo8++mir7U+cOJEePXrQp08ftm7dyooVK/jTn/5EoVBgy5YtjB8/HoCpU6cyYcIEunfvzo033thq7OPyCMHMrBXdunX7aPm73/0ukyZN4oEHHtgh/uyzzwLw+9//nsMPP7zV2O7aBzjiiCMoFArU1dUxY8YMhg4dynvvvUdDQwPz589n3Lhx3H333S3GyiGVEYKkvsDjETFE0kzgKOCxiPh+8nlJMTOzSvGVr3yF6667jrvuuguAtWvXAvDcc89RKBTo168fEyZMYM6cObz++uuMGTOGrl27Mm/evJL7OPbYY/nMZz7D6NGj2bp1K9OmTWO//fZj5cqVjBgxgi1btjB9+vQWY+WgiChLQzs0Kt0LHA/8M3BaRHxR0h3Aj4BjSolFxMpd9VFdXR3t9XkIPsvILHt/+MMfOOqoo9q0zVVXXUWhUNhhsviee+4B4Itf/GL5kttDLX1Pkuojorql9cs+QpB0IvAu8CZQABrL4xNADTCkxNguC4KZWd6uuuqqnWKVUAj2VFnnECR1Aa4ELktC3YC1yfJmoG8bYi21XytpuaTlDQ0N5UzdzKzDK/ek8mXA7RGxMXn/DrBvstw96a/U2E4iYnpEVEdEdVVVi48ENTOzPVTugjAWmCKpDjgWmEDx8A/AYGAVUF9izMzMMlTWOYSIGNW4nBSF04Clkg4GTgaGAVFizMysYhz3T3PK2l79/zm/rO2VQ2rXIUREISI2U5xYfhoYExGbSo2llZeZ2d5ow4YNnHLKKYwcOZKLLroolT5SvzAtIjZExLyIeLOtMTMzK7r33ns599xzWbp0KW+//TZpnHbvK5XNzPYCvXv3ZsWKFWzcuJHXX3+dAQMGlL0P38vIzKwCffWrX2XFihUfvR8zZgwrV67kxz/+MUceeSS9evUqe58uCGZmFWjatGk7vD/nnHO466672H///bnpppuYNWsWtbW1Ze3Th4zMzPYC7733Hi+99BIffvghzzzzTCrPf/YIwcysBHmfJnr55ZdzwQUXsHr1aoYPH85ZZ51V9j5cEMzM9gJDhw7llVdeSbUPHzIyMzPABcHMzBIuCGZmBrggmJlZwpPKZmYleO2aY8ra3oArXypre+XgEYKZWYVat24dI0eOzKw/FwQzswq0YcMGvvCFL/Duu+9m1qcLgplZBerUqRNz585l//33z6xPzyGYmVWgLAtBo1RGCJIOkDRO0oFptG9mZuVX9oIg6SDgV8BQYJGkKkmvSapLXsck610t6TlJtzXZdqeYmZllI41DRp8FvhERT0vqBXwJeCAiLm1cQVI1UEOxaFwqaSywsXksIhamkJ+ZWZtV4mmi5Vb2EUJELEyKwSiKO/f3gYmSlkm6X9I+wCjg4YgIYCEwspWYmVmHVldXl1lfac0hCDgT+AB4ARgdETUURwGnAN2Atcnqm4G+rcSat1srabmk5Q0NDWmkbmbWYaVSEKJoCvAk0C8i3kg+ehUYCLwD7JvEuid5tBRr3u70iKiOiOqqqqo0Ujcz67DSmFS+VFLjkyR6AndJGiypEzCR4oihnuJ8AcBgYFUrMTMzy0gak8rTgXmSLgRepjg3cD8g4BcRsVDSJ4DrJN0CnJS8VrcQMzOzjJS9IETEBmBcs/CgZuv8R3Jm0anALRHxZ4CWYmZmlWDErSPK2t5vL/ltWdsrh9yuVI6I94GHdhczM+uINm3axOc//3m2bdtG9+7dmTt3Ll26dEm1T9/LyMysAt1///1885vfZMGCBfTr14/HH3889T59LyMzswp08cUXf7Tc0NBAnz59Uu/TIwQzswr21FNPsWHDBoYNG5Z6Xx4hmJlVqLfeeotLLrmEhx9+OJP+PEIwM6tAW7du5YwzzuC6667jkEMOyaRPjxDMzEqQ9WmiM2fOpL6+nqlTpzJ16lQmT57MmWeemWqfLghmZhVo8uTJTJ48OdM+fcjIzMwAFwQzM0u4IJiZGeCCYGZmCU8qm5mVYPGo0WVtb/SSxbtd56233qK+vp4hQ4Zw4IEHlrX/lniEYGZWgd544w1OPfVUnn32WcaMGUMWT4n0CMHMrAK98sor/OhHP2LYsGFs2LCB3/3ud4wfPz7VPj1CMDOrQGPHjmXYsGEsWbKEZ599luHDh6feZyoFQdIBksZJSv+gl5lZOxURzJ07l86dO9OpU6fU+0vjmcoHAb8ChgKLJFVJminpSUnfabJeSTEzs45KErfffjsnnHACjz76aOr9pTFC+CzwjYiYCvwaOBHoFBEnAAdLGijp9FJiKeRmZrZXuOGGG5gzZw4AGzdupGfPnqn3mcYzlRcCSBpFcZRwADAv+fgJoAYYUmJsZbnzMzPbE6WcJlpOtbW1nHHGGcyYMYOjjz6az33uc6n3mcpZRpIEnAl8AAhYm3y0GTgc6FZirHm7tUAtwIABA9JI3cysIvTq1YsFCxZk2mcqk8pRNAV4EhgG7Jt81D3p850SY83bnR4R1RFRXVVVlUbqZmYdVhqTypdKOj952xO4nuLhH4DBwCqgvsSYmZllJI1DRtOBeZIuBF4G/hVYIulg4GSKI4YAlpYQMzOzjKQxqbwBGNc0JqmQxH4QEZvaEjMzs2xkcuuKpEjM25OYmVkluO1bvyxre1+7cUJZ2ysH37rCzKyCrVu3jiFDhmTSlwuCmVkF+/a3v83777+fSV8uCGZmFeqJJ56gW7du9OvXL5P+XBDMzCrQ1q1bueaaa7j++usz69MFwcysAl1//fVMmTIlk3sYNXJBMDOrQAsXLuT222+nUCjw/PPPc+GFF6bep5+YZmZWgqxPE12yZMlHy4VCgRkzZqTep0cIZmYVrq6uLpN+XBDMzAxwQTAzs4QLgpmZAS4IZmaW8FlGZmYlmHrupLK2d8V9D5W1vXLwCMHMrAJt27aNAQMGUCgUKBQKvPTSS6n36RGCmVkFevHFFznrrLO44YYbMuvTIwQzswr09NNP88gjj1BTU8M555zDtm3bUu8zjWcqf0rSfEkLJD0iqYuk1yTVJa9jkvWulvScpNuabLtTzMysIzr++ONZvHgxy5Yto2fPnjz22GOp95nGCOEc4KaIGAe8CVwGPBARheT1kqRqoAYYCqyRNLalWAq5mZntFQYNGsRBBx0EwJFHHsnKlStT77PsBSEi7oiIBcnbKmAbMFHSMkn3S9oHGAU8HBEBLARGthLbgaRaScslLW9oaCh36mZmFeO8887jhRde4MMPP+SRRx5h8ODBqfeZ2qSypOFAL2ABMCsi3pB0O3AK0A34t2TVzUBfioWjeWwHETEdmA5QXV0daeVuZtZc1qeJXnnllZx99tlEBKeddhpjx6Z/0CSVgiDpAOBW4L8Bb0bE35KPXgUGAu8A+yax7hRHKi3FzMw6pKOPPpoXX3wx0z7TmFTuAswDLo+I1cC9kgZL6gRMBF4A6inOFwAMBla1EjMzs4ykMUL4MnAccIWkK4BFwL2AgF9ExEJJnwCuk3QLcFLyWt1CzMwsMxGBpLzTKIvidGzblL0gRMSdwJ3Nwlc3W+c/krOITgVuiYg/A7QUMzPLQteuXVm/fj29e/fe64tCRLB+/Xq6du3apu32qCBIqomIZXuybaOIeB94aHcxM7Ms9O/fnzVr1tBezmDs2rUr/fv3b9M2JRUESQuS6woaXUcLp4Wame2tOnfuzGGHHZZ3GrnaZUGQNAgYAnxa0vlJuBuwJe3EzMwsW7s7y0gtfF0PnJFaRmZmlotdjhAi4gXgBUlHRMScjHIyM7MclDqpfLOkzwNdGgMuEGZm7UupF6Y9DvSneMio8WVmZu1IqSOEzRHxw1QzMTOzXJVaEJZJegCYA7wLEBFLUsvKzMwyV2pB+IDijemOp3i4KAAXBDOzdqTUgrCKYhFoLAZmZtbOtOVup6J4e+rTKT7MxszM2pGSRggRMbvJ27sk3ZFSPmZmlpNS72XUdESwP/DZdNIxM7O8lDqHMIbtcwdbgYvTScfMzPJS6hzCtcA64ADgr8CK1DIyM7NclFoQ/gXoA8wHPg3MSi0jMzPLRamHjD4TEecly7+WtLi1FSV9Cngwafsd4EyKT1A7CngsIr6frDezlJiZmWWj1BHCv0u6XNKJyXOS1+5i3XOAm5IH6rwJfB7oFBEnAAdLGijp9FJie/5tmZlZW5VaEC6i+Bf/JGAz8NXWVoyIOyJiQfK2CjgXmJe8fwKoAQolxnYgqVbScknL28tj7szMKkWpBeE+4LWIuBjoQXFOYZckDQd6Aa+zfUSxGehL8alrpcR2EBHTI6I6IqqrqqpKTN3MzEpRakHo1XhxWkRcCxy4q5UlHQDcCnyJ4jzCvslH3ZM+S42ZmVlGSt3prpF0qaQxkv4X8JfWVpTUheKhn8sjYjVQz/bDP4Mp3hep1JiZmWWk1LOMvgjUUpxDeBU4fxfrfhk4DrgimYCeBZwn6WDgZGAYxYvclpYQMzOzjJR6L6O/UTwEVMq6d1I8zfQjkn4BjAN+EBGbklihlJiZmWWj1BHCxxIRG9h+BlGbYmZmlg1P3JqZGeCCYGZmCRcEMzMDXBDMzCzhgmBmZoALgpmZJVwQzMwMcEEwM7OEC4KZmQEuCGZmlnBBMDMzwAXBzMwSLghmZga4IJiZWcIFwczMABcEMzNLpFIQJPWVtDRZ/rSkNZLqkldVEp8p6UlJ32my3U4xMzPLRtkLgqRewGygWxL6B2BqRBSSV4Ok04FOEXECcLCkgS3Fyp2bmZm1Lo0RwofAmcDm5P0w4GJJT0n6URIrsP1RmU8ANa3EdiCpVtJyScsbGhpSSN3MrOMqe0GIiM0RsalJaD5wQkQMB/5O0iCKo4e1yeebgb6txJq3PT0iqiOiuqqqqtypm5l1aPtk0MeTEfG3ZPlVYCDwDrBvEutOsTC1FDMzs4xksdP9taSDJO0HjAdeBurZfkhoMLCqlZiZmWUkixHC1cAiYCtwV0SskPQGsFTSwcDJFOcZooWYmZllJLWCEBGF5Osi4Mhmn22WVADGAT9onHNoKWZmZtnIYoTQoojYwPazilqNmZlZNjxxa2ZmgAuCmZklXBDMzAxwQTAzs4QLgpmZAS4IZmaWcEEwMzPABcHMzBK5XZhmpZt67qSdYlfc91AOmZhZe+YRgpmZAS4IZmaWcEEwMzPABcHMzBIuCGZmBrggmJlZwgXBzMyAlAqCpL6SlibLnSU9KulJSV9qS8zMzLJT9oIgqRcwG+iWhC4BlkfECcA/SurRhpiZmWUkjSuVPwTOBH6evC8AlyXLTwLVbYgtatqwpFqgFmDAgAEppJ6PxaNG7xg4/tv5JGJmHVrZRwgRsTkiNjUJdQPWJsubgb5tiDVve3pEVEdEdVVVVblTNzPr0LKYVH4H2DdZ7p70WWrMzMwyksVOtx6oSZYHA6vaEDMzs4xkcbfT2cBjkkYCfw88Q/HQUCkxMzPLSGojhIgoJF9XA+OA3wJjI+LDUmNp5WZmZjvL5HkIEfHvwLw9iZmZWTY8cWtmZoALgpmZJVwQzMwMcEEwM7OEC4KZmQEuCGZmlnBBMDMzwAXBzMwSLghmZga4IJiZWcIFwczMABcEMzNLuCCYmRnggmBmZgkXBDMzA1wQzMwskXpBkLSPpNck1SWvYyRdLek5Sbc1WW+nmJmZZSeLEcIg4IGIKCSP1fwkUAMMBdZIGiupunksg7zMzKyJLB6hOQyYKGkEsBp4AXg4IkLSQmACsKmF2MLmDUmqBWoBBgwYkEHqZmYdRxYjhOeA0RFRA2wE9gXWJp9tBvoC3VqI7SQipkdEdURUV1VVpZq0mVlHk8UI4cWI+Fuy/CrQhWJRAOhOsSi900LMzMwylMWO915JgyV1AiZSHA3UJJ8NBlYB9S3EzMwsQ1mMEK4BfgII+AXwfWCppFuAk5LXauC6ZrF2acStI3aKXZvJP4OZ2a6lvieKiJcpnmn0keQsolOBWyLiz63FzMwsO7n8aRoR7wMP7S5mZmbZ8eStmZkBLghmZpZwQTAzM8AFwczMEi4IZmYGuCCYmVnCBcHMzAAXBDMzS/ieCVbRbvvWL3d4/7UbJ+SUiVn75xGCmZkBLghmZpbwISOz3Wh+2Ap86MraJxcEa5F3gmYdjwtCyl675pgdA732zycRM7Pd8ByCmZkBHiFYhVk8avSOgeO/vcPbqedO2mmbK+7zYzQse+3xsGrFFQRJM4GjgMci4vt552PbNd8Zt9cd8e6KEuTzs6iEazLa407QtquogiDpdKBTRJwg6Q5JAyNiZd55leq4f5qzU+yRHjkkUoLmz3a+9qfNfhVa2AmmnQPk83zpnX4WlfXfIjMt/nuU8HvRUYtjSyrhZ7Fp3eyd1ik1D0VEWZIqB0k/Bh6PiMckTQJ6RMSsJp/XArXJ2yOAFR+zywOBv37MNsqhEvKohBygMvJwDttVQh6VkANURh7lyOGQiKhq6YNK+1OoG7A2Wd4MHN70w4iYDkwvV2eSlkdEdbna25vzqIQcKiUP51BZeVRCDpWSR9o5VNpZRu8A+ybL3am8/MzM2q1K2+HWAzXJ8mBgVX6pmJl1LJV2yOhfgaWSDgZOBoal3F/ZDj99TJWQRyXkAJWRh3PYrhLyqIQcoDLySDWHippUBpDUCxgHLImIN/POx8yso6i4gmBmZvmotDkE66AkHSBpnKQD887FrKPqsAVBUi9Jj0laKumuHPPoK2lpjv1/StJ8SQskPSKpSw45HAT8ChgKLJLU4jnSGebTV9Lvc+p7H0mvSapLXsfsfqtU87lDUi5XfUma3OTn8LykaTnlkfu+QtJhkn6V5HBjWv102IIAnAfcFxEjgR6SMj+/OJkvmU3x+ou8nAPcFBHjgDeBk3LI4bPANyJiKvBr4L/kkENTP2T76c9ZGwQ8EBGF5PVSTnkgaSTQLyJ2vl9FBiLizsafA7CU/CZ1c99XADcA/zvJob+kQhqddOSCsB44QlJP4DPAaznk8CFwJsWL8HIREXdExILkbRXwlxxyWBgRT0saRXGU8FTWOTSSdCLwLsXimIdhwERJyyTdLymXMwEldQbuBlZJ+q955NAkl08DfSOiPqcUKmFf8XfA75LlvwCfSqOTDlMQJE1rMvyso3gV9EDgfwCvAhuyzikiNkfEpqz7bYmk4UCviHg6p/5FsTh+QLFQ5pFDF+BK4LI8+k88B4yOiBpgI3BKTnmcD/xf4AfAUEmX5JQHwBTgzhz7X0bO+wrgIeB7yeG7k4DfpNFJhykIEfHVJsPwAsWKe1FEXEPxH/mCXBPMkaQDgFuBL+WVQxRNAZ4E/jGnNC4Dbo+IjTn1D/BiRLyRLL9KcUeUhyHA9OTU7/uAMXkkIekTwJiIWJRH/4lryXlfkdz5eT5wITA7It5Jo58OUxBasB9wjKROwD8AHfL82+Sv4nnA5RGxOqccLpV0fvK2J8W/jPMwFpiSjCCPlTQjhxzulTQ4+b2cCLyQQw4AfwT+U7JcDeTyuwGMBJ7Jqe9GlbKveB4YANyUVgcd9joESUOBWcAhFI9ZT0yr6paQS10yasmj78kU/wJq3PHcGRFzM86hF8Wi9EngZWBK5PyLmde/iaSjgZ8AAn4REVdknUOSRw/gX4C+QGdgUkSs3fVWqeRxLbA8In6Wdd9NcqiIfYWkq4E/RsS9qfXRUQuCmZntqCMfMjIzsyZcEMzMDHBBMDOzhAuCmZkBLghmZpZwQbAOL7nuoHns5hK3LWm9Etv6lKQnkqvpJ5arXbNS+bRT6/DyvA6kWR6jgM9FxHfyzsU6pkp7hKZZWSR/9T8HDIqI8ZL2A+YAfYCXkttk7HL7xiIhaV/gZ0Bv4N+S7a9tYb1PAvcABwNrKN7i4J8pXthVQ/GGZCe19CRASf8zWb+npBrgv0dEQynfR/IMiXkUR/ydgSsioq6tPzMzHzKy9moY8FREjE/e1wIvR8Qo4CBJg9rQ1pEUd/AjgP/cWAxa8JWkj9HA/2P7vaEOT2I/AU5sacOIuAX4OnBPcr+thjZ8H7XAz5LClNudc23v54Jg7dXLzW53cATF20rXUbxHz6fb0NZa4DhgCXDLLtb7e7bfd+cZ4KhkeU7y9S9AWx9AVMr3MQB4Jfn8+Ta2b/YRFwRrr5rfa2YFcHPyV/R3aNs97U+i+HCS4RFx/y7We4XiX/QkXxt30u+2oa/mSvk+/gQ0PlntuI/Rl3VwLgjWUdwNnCxpCXAR8Hobtv09cGtyBtCDyQ3oWjID+GzSx0CK8wnl1tL3cTdwmqRFFG8QaLZHfJaR2W5I+gpwFsWH93wA/LBSJ20lXQXUVWp+VtlcEMwy1sJ1D5siItfHVJqBC4KZmSU8h2BmZoALgpmZJVwQzMwMcEEwM7PE/wfkp1L0CJeO2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='religion_freq', hue='happiness', data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 连续性数值数据\n",
    "### 收入相关\n",
    "1. income 被调查者收入\n",
    "2. inc_exp 期望收入\n",
    "3. family_income 家庭收入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>inc_exp</th>\n",
       "      <th>family_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.096800e+04</td>\n",
       "      <td>1.096800e+04</td>\n",
       "      <td>1.096700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.099567e+04</td>\n",
       "      <td>2.110474e+05</td>\n",
       "      <td>6.632110e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.002225e+05</td>\n",
       "      <td>2.589083e+06</td>\n",
       "      <td>2.835960e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.615000e+03</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>1.300000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>3.840000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.500000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>7.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999990e+06</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>9.999992e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             income       inc_exp  family_income\n",
       "count  1.096800e+04  1.096800e+04   1.096700e+04\n",
       "mean   3.099567e+04  2.110474e+05   6.632110e+04\n",
       "std    2.002225e+05  2.589083e+06   2.835960e+05\n",
       "min   -3.000000e+00 -3.000000e+00  -3.000000e+00\n",
       "25%    1.615000e+03  1.000000e+04   1.300000e+04\n",
       "50%    1.500000e+04  4.000000e+04   3.840000e+04\n",
       "75%    3.500000e+04  8.000000e+04   7.000000e+04\n",
       "max    9.999990e+06  1.000000e+08   9.999992e+06"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['income', 'inc_exp', 'family_income']\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False)\n",
    "ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
    "sns.kdeplot(data['income'],ax=ax)\n",
    "plt.xlim((-4,4000000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False)\n",
    "ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
    "sns.kdeplot(data['inc_exp'],ax=ax)\n",
    "plt.xlim((-4,4000000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False)\n",
    "ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
    "sns.kdeplot(data['family_income'],ax=ax)\n",
    "plt.xlim((-4,4000000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    258\n",
       "-3    191\n",
       "-2    156\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['income']<0]['income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0    1328\n",
       "-3.0     173\n",
       "Name: inc_exp, dtype: int64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['inc_exp']<0]['inc_exp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0    583\n",
       "-3.0    298\n",
       "-1.0     49\n",
       "Name: family_income, dtype: int64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['family_income']<0]['family_income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 收入小于0的异常数据都用0替代\n",
    "for col in cols:\n",
    "    data.loc[:,col] = data.loc[:, col].apply(lambda x: x if x>0 else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把无用特征删除\n",
    "#survey_type,  province, city, county, survey_time, gender, nationality,religion,religion_freq,property_other,edu_other,invest_other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "dcols = ['survey_type','province', 'city', 'county', 'survey_time', 'gender', 'nationality',\n",
    "         'religion','religion_freq','property_other','invest_other']\n",
    "data_1 = data.copy()\n",
    "data_1.drop(dcols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    10968\n",
       "Name: time, dtype: int64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1['time'].apply(lambda x: x.year).value_counts()  # 都是2015年，删除该列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1.drop('time',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>edu_yr</th>\n",
       "      <th>work_yr</th>\n",
       "      <th>f_birth</th>\n",
       "      <th>m_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>8212.000000</td>\n",
       "      <td>4029.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1964.602753</td>\n",
       "      <td>1984.900999</td>\n",
       "      <td>14.447009</td>\n",
       "      <td>1115.477206</td>\n",
       "      <td>1154.928519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.897683</td>\n",
       "      <td>18.134951</td>\n",
       "      <td>11.397224</td>\n",
       "      <td>961.361182</td>\n",
       "      <td>955.741708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1920.000000</td>\n",
       "      <td>1931.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1972.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1965.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1922.000000</td>\n",
       "      <td>1926.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1977.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1947.000000</td>\n",
       "      <td>1949.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth       edu_yr      work_yr       f_birth       m_birth\n",
       "count  10968.000000  8212.000000  4029.000000  10968.000000  10968.000000\n",
       "mean    1964.602753  1984.900999    14.447009   1115.477206   1154.928519\n",
       "std       16.897683    18.134951    11.397224    961.361182    955.741708\n",
       "min     1920.000000  1931.000000    -3.000000     -3.000000     -3.000000\n",
       "25%     1952.000000  1972.000000     5.000000     -2.000000     -2.000000\n",
       "50%     1965.000000  1984.000000    12.000000   1922.000000   1926.000000\n",
       "75%     1977.000000  2000.000000    22.000000   1947.000000   1949.000000\n",
       "max     1997.000000  2015.000000    55.000000   1979.000000   1985.000000"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# birth, edu_yr,work_yr,f_birth,m_birth\n",
    "data_1[['birth', 'edu_yr', 'work_yr', 'f_birth', 'm_birth']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把birth替换为年代，比如1920-1929，就是20年代用2表示，1991-1999用9表示\n",
    "def replace_birth(x):\n",
    "    return (x-1900)//10\n",
    "\n",
    "data_1.loc[:, 'birth_r'] = data_1.loc[:, 'birth'].apply(replace_birth)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# edu_yr 采取和birth同样的操作\n",
    "data_1.loc[:, 'edu_yrr'] = data_1.loc[:, 'edu_yr'].apply(replace_birth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# work_yr 空值和小于0的值都替换为0，同时转换为整型,工作5年以内的用1表示，每增加5年加一\n",
    "data_1.loc[:, 'work_yrr'] = data_1.loc[:, 'work_yr'].apply(lambda x: 0 if pd.isnull(x) or x<=0 else int(x)//5+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6304.000000\n",
       "mean     1942.267449\n",
       "std        17.761920\n",
       "min      1850.000000\n",
       "25%      1930.000000\n",
       "50%      1943.000000\n",
       "75%      1956.000000\n",
       "max      1979.000000\n",
       "Name: f_birth, dtype: float64"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['f_birth']>0]['f_birth'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6520.000000\n",
       "mean     1944.220706\n",
       "std        17.141442\n",
       "min      1851.000000\n",
       "25%      1932.000000\n",
       "50%      1945.000000\n",
       "75%      1958.000000\n",
       "max      1985.000000\n",
       "Name: m_birth, dtype: float64"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['m_birth']>0]['m_birth'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_birth_fm(x):\n",
    "    return (x-1850)//10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f_birth, m_birth, 父母的出生年龄， 小于等于0的用0表示，其他采取 和birth相同的操作\n",
    "data_1.loc[:, 'f_birth1'] = data_1.loc[:, 'f_birth'].apply(lambda x: 0 if  x<=0 else replace_birth_fm(x))\n",
    "data_1.loc[:, 'm_birth1'] = data_1.loc[:, 'm_birth'].apply(lambda x: 0 if  x<=0 else replace_birth_fm(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除被替换的列\n",
    "cols = ['birth', 'edu_yr', 'work_yr', 'f_birth', 'm_birth']\n",
    "data_1.drop(cols,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s_birth\n",
    "data_1.loc[:,'s_birth_r'] = data_1.loc[:, 's_birth'].apply(replace_birth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "# marital_1st, marital_now\n",
    "data_1.loc[:,'marital_1st'] = data_1.loc[:, 'marital_1st'].apply(replace_birth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1.loc[:,'marital_now'] = data_1.loc[:, 'marital_now'].apply(replace_birth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1.drop('s_birth', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "# join_party\n",
    "data_1.loc[:,'join_party'] = data_1.loc[:, 'join_party'].apply(replace_birth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 其他数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1.select_dtypes(include='object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['happiness', 'edu_status', 'join_party', 'floor_area', 'hukou_loc',\n",
       "       'social_neighbor', 'social_friend', 'work_status', 'work_type',\n",
       "       'work_manage', 'family_income', 'minor_child', 'marital_1st', 's_birth',\n",
       "       'marital_now', 's_edu', 's_political', 's_hukou', 's_income',\n",
       "       's_work_exper', 's_work_status', 's_work_type', 'inc_exp',\n",
       "       'public_service_2', 'public_service_5', 'edu_yrr'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1.select_dtypes(include='float').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>happiness</th>\n",
       "      <th>edu_status</th>\n",
       "      <th>join_party</th>\n",
       "      <th>floor_area</th>\n",
       "      <th>hukou_loc</th>\n",
       "      <th>social_neighbor</th>\n",
       "      <th>social_friend</th>\n",
       "      <th>work_status</th>\n",
       "      <th>work_type</th>\n",
       "      <th>work_manage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8000.000000</td>\n",
       "      <td>9399.000000</td>\n",
       "      <td>1126.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10964.000000</td>\n",
       "      <td>9871.000000</td>\n",
       "      <td>9871.000000</td>\n",
       "      <td>4029.000000</td>\n",
       "      <td>4030.000000</td>\n",
       "      <td>4030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.868125</td>\n",
       "      <td>3.497925</td>\n",
       "      <td>1986.476021</td>\n",
       "      <td>115.983901</td>\n",
       "      <td>1.371124</td>\n",
       "      <td>3.469760</td>\n",
       "      <td>3.674197</td>\n",
       "      <td>3.243237</td>\n",
       "      <td>1.115881</td>\n",
       "      <td>2.876179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.818118</td>\n",
       "      <td>1.140376</td>\n",
       "      <td>17.505069</td>\n",
       "      <td>90.882269</td>\n",
       "      <td>0.671741</td>\n",
       "      <td>2.024989</td>\n",
       "      <td>1.788294</td>\n",
       "      <td>1.401838</td>\n",
       "      <td>0.320122</td>\n",
       "      <td>0.783488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>1942.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>64.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>97.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         happiness   edu_status   join_party    floor_area     hukou_loc  \\\n",
       "count  8000.000000  9399.000000  1126.000000  10968.000000  10964.000000   \n",
       "mean      3.868125     3.497925  1986.476021    115.983901      1.371124   \n",
       "std       0.818118     1.140376    17.505069     90.882269      0.671741   \n",
       "min       1.000000    -8.000000  1942.000000      0.000000      1.000000   \n",
       "25%       4.000000     4.000000  1973.000000     64.600000      1.000000   \n",
       "50%       4.000000     4.000000  1986.000000     97.800000      1.000000   \n",
       "75%       4.000000     4.000000  2002.000000    134.000000      2.000000   \n",
       "max       5.000000     4.000000  2015.000000   2400.000000      4.000000   \n",
       "\n",
       "       social_neighbor  social_friend  work_status    work_type  work_manage  \n",
       "count      9871.000000    9871.000000  4029.000000  4030.000000  4030.000000  \n",
       "mean          3.469760       3.674197     3.243237     1.115881     2.876179  \n",
       "std           2.024989       1.788294     1.401838     0.320122     0.783488  \n",
       "min           1.000000       1.000000     1.000000     1.000000     1.000000  \n",
       "25%           2.000000       2.000000     3.000000     1.000000     2.000000  \n",
       "50%           3.000000       3.000000     3.000000     1.000000     3.000000  \n",
       "75%           5.000000       5.000000     3.000000     1.000000     3.000000  \n",
       "max           7.000000       7.000000     9.000000     2.000000     4.000000  "
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = data_1.select_dtypes(include='float').columns\n",
    "data_1[cols[:10]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family_income</th>\n",
       "      <th>minor_child</th>\n",
       "      <th>marital_1st</th>\n",
       "      <th>marital_now</th>\n",
       "      <th>s_edu</th>\n",
       "      <th>s_political</th>\n",
       "      <th>s_hukou</th>\n",
       "      <th>s_income</th>\n",
       "      <th>s_work_exper</th>\n",
       "      <th>s_work_status</th>\n",
       "      <th>s_work_type</th>\n",
       "      <th>inc_exp</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>edu_yrr</th>\n",
       "      <th>s_birth_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.096800e+04</td>\n",
       "      <td>9520.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.00000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>8.601000e+03</td>\n",
       "      <td>8601.000000</td>\n",
       "      <td>3524.000000</td>\n",
       "      <td>3524.000000</td>\n",
       "      <td>1.096800e+04</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>8212.000000</td>\n",
       "      <td>8601.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.631525e+04</td>\n",
       "      <td>0.461870</td>\n",
       "      <td>-26.363330</td>\n",
       "      <td>-45.03182</td>\n",
       "      <td>4.672131</td>\n",
       "      <td>1.339379</td>\n",
       "      <td>1.846646</td>\n",
       "      <td>2.818087e+04</td>\n",
       "      <td>2.829903</td>\n",
       "      <td>3.311862</td>\n",
       "      <td>1.116345</td>\n",
       "      <td>2.110477e+05</td>\n",
       "      <td>68.274207</td>\n",
       "      <td>63.268827</td>\n",
       "      <td>8.046396</td>\n",
       "      <td>5.942565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.835837e+05</td>\n",
       "      <td>0.752218</td>\n",
       "      <td>75.403114</td>\n",
       "      <td>88.30704</td>\n",
       "      <td>2.927166</td>\n",
       "      <td>0.929513</td>\n",
       "      <td>1.322116</td>\n",
       "      <td>1.755756e+05</td>\n",
       "      <td>1.730981</td>\n",
       "      <td>1.294354</td>\n",
       "      <td>0.320684</td>\n",
       "      <td>2.589083e+06</td>\n",
       "      <td>20.393884</td>\n",
       "      <td>23.008494</td>\n",
       "      <td>1.845359</td>\n",
       "      <td>1.478278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-191.000000</td>\n",
       "      <td>-191.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.300000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-191.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.840000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999992e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.999999e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       family_income  minor_child   marital_1st  marital_now        s_edu  \\\n",
       "count   1.096800e+04  9520.000000  10968.000000  10968.00000  8601.000000   \n",
       "mean    6.631525e+04     0.461870    -26.363330    -45.03182     4.672131   \n",
       "std     2.835837e+05     0.752218     75.403114     88.30704     2.927166   \n",
       "min     0.000000e+00     0.000000   -191.000000   -191.00000     1.000000   \n",
       "25%     1.300000e+04     0.000000      6.000000   -191.00000     3.000000   \n",
       "50%     3.840000e+04     0.000000      8.000000      8.00000     4.000000   \n",
       "75%     7.000000e+04     1.000000      9.000000      9.00000     6.000000   \n",
       "max     9.999992e+06     6.000000     11.000000     11.00000    14.000000   \n",
       "\n",
       "       s_political      s_hukou      s_income  s_work_exper  s_work_status  \\\n",
       "count  8601.000000  8601.000000  8.601000e+03   8601.000000    3524.000000   \n",
       "mean      1.339379     1.846646  2.818087e+04      2.829903       3.311862   \n",
       "std       0.929513     1.322116  1.755756e+05      1.730981       1.294354   \n",
       "min       1.000000     1.000000 -3.000000e+00      1.000000       1.000000   \n",
       "25%       1.000000     1.000000  0.000000e+00      1.000000       3.000000   \n",
       "50%       1.000000     1.000000  1.200000e+04      3.000000       3.000000   \n",
       "75%       1.000000     2.000000  3.000000e+04      5.000000       3.000000   \n",
       "max       4.000000     8.000000  8.999999e+06      6.000000       9.000000   \n",
       "\n",
       "       s_work_type       inc_exp  public_service_2  public_service_5  \\\n",
       "count  3524.000000  1.096800e+04      10968.000000      10968.000000   \n",
       "mean      1.116345  2.110477e+05         68.274207         63.268827   \n",
       "std       0.320684  2.589083e+06         20.393884         23.008494   \n",
       "min       1.000000  0.000000e+00          0.000000          0.000000   \n",
       "25%       1.000000  1.000000e+04         60.000000         60.000000   \n",
       "50%       1.000000  4.000000e+04         70.000000         70.000000   \n",
       "75%       1.000000  8.000000e+04         80.000000         80.000000   \n",
       "max       2.000000  1.000000e+08        100.000000        100.000000   \n",
       "\n",
       "           edu_yrr    s_birth_r  \n",
       "count  8212.000000  8601.000000  \n",
       "mean      8.046396     5.942565  \n",
       "std       1.845359     1.478278  \n",
       "min       3.000000     0.000000  \n",
       "25%       7.000000     5.000000  \n",
       "50%       8.000000     6.000000  \n",
       "75%      10.000000     7.000000  \n",
       "max      11.000000    10.000000  "
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1[cols[10:]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 除了floor_area, family_income, s_income, nc_exp，其他都需要转换成整型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_cols = data_1.select_dtypes(exclude=['float', 'object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['edu', 'income', 'political', 'property_0', 'property_1', 'property_2',\n",
       "       'property_3', 'property_4', 'property_5', 'property_6',\n",
       "       ...\n",
       "       'public_service_6', 'public_service_7', 'public_service_8',\n",
       "       'public_service_9', 'property_9', 'invest_9', 'birth_r', 'work_yrr',\n",
       "       'f_birth1', 'm_birth1'],\n",
       "      dtype='object', length=103)"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edu</th>\n",
       "      <th>income</th>\n",
       "      <th>political</th>\n",
       "      <th>property_0</th>\n",
       "      <th>property_1</th>\n",
       "      <th>property_2</th>\n",
       "      <th>property_3</th>\n",
       "      <th>property_4</th>\n",
       "      <th>property_5</th>\n",
       "      <th>property_6</th>\n",
       "      <th>property_7</th>\n",
       "      <th>property_8</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_jin</th>\n",
       "      <th>health</th>\n",
       "      <th>health_problem</th>\n",
       "      <th>depression</th>\n",
       "      <th>hukou</th>\n",
       "      <th>media_1</th>\n",
       "      <th>media_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>110</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>122</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>6420</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>170</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>110</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>3</td>\n",
       "      <td>1720</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10965</th>\n",
       "      <td>4</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>1</td>\n",
       "      <td>15000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10967</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10968</th>\n",
       "      <td>4</td>\n",
       "      <td>12000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>110</td>\n",
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       "<p>10968 rows × 20 columns</p>\n",
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       "       edu  income  political  property_0  property_1  property_2  property_3  \\\n",
       "id                                                                              \n",
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       "...    ...     ...        ...         ...         ...         ...         ...   \n",
       "10964    3    1720          1           0           0           0           0   \n",
       "10965    4   10000          1           0           1           0           0   \n",
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       "10967    7       0          1           0           1           0           0   \n",
       "10968    4   12000          1           0           1           1           0   \n",
       "\n",
       "       property_4  property_5  property_6  property_7  property_8  height_cm  \\\n",
       "id                                                                             \n",
       "1               0           0           0           0           0        176   \n",
       "2               1           0           0           0           0        170   \n",
       "3               0           0           0           0           0        160   \n",
       "4               0           0           0           0           0        163   \n",
       "5               1           0           0           0           0        165   \n",
       "...           ...         ...         ...         ...         ...        ...   \n",
       "10964           0           0           0           0           1        170   \n",
       "10965           0           0           0           0           0        150   \n",
       "10966           0           0           0           0           0        165   \n",
       "10967           0           0           0           0           0        168   \n",
       "10968           0           0           0           0           0        164   \n",
       "\n",
       "       weight_jin  health  health_problem  depression  hukou  media_1  media_2  \n",
       "id                                                                              \n",
       "1             155       3               2           5      5        4        2  \n",
       "2             110       5               4           3      1        2        2  \n",
       "3             122       4               4           5      1        2        2  \n",
       "4             170       4               4           4      1        2        1  \n",
       "5             110       5               5           3      2        1        3  \n",
       "...           ...     ...             ...         ...    ...      ...      ...  \n",
       "10964         150       1               3           3      1        2        2  \n",
       "10965         100       4               4           3      1        1        1  \n",
       "10966         110       2               3           4      1        1        1  \n",
       "10967         120       4               5           5      1        1        1  \n",
       "10968         110       5               5           5      2        4        2  \n",
       "\n",
       "[10968 rows x 20 columns]"
      ]
     },
     "execution_count": 316,
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    "data_1[int_cols[:20]]\n",
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      "text/plain": [
       "       media_3  media_4  media_5  media_6  leisure_1  leisure_2  leisure_3  \\\n",
       "id                                                                           \n",
       "1            5        5        4        3          1          4          3   \n",
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       "...        ...      ...      ...      ...        ...        ...        ...   \n",
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       "10967        1        5        1        1          1          5          5   \n",
       "10968        1        4        1        1          1          5          5   \n",
       "\n",
       "       leisure_4  leisure_5  leisure_6  leisure_7  leisure_8  leisure_9  \\\n",
       "id                                                                        \n",
       "1              1          2          3          4          1          4   \n",
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       "4              4          5          4          5          1          1   \n",
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       "...          ...        ...        ...        ...        ...        ...   \n",
       "10964          5          5          4          4          5          4   \n",
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       "10967          5          5          5          5          5          5   \n",
       "10968          2          5          4          4          5          4   \n",
       "\n",
       "       leisure_10  leisure_11  leisure_12  socialize  relax  learn  \\\n",
       "id                                                                   \n",
       "1               5           4           1          2      4      3   \n",
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       "\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10965</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10967</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10968</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10968 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       equity  class  class_10_before  class_10_after  class_14  work_exper  \\\n",
       "id                                                                            \n",
       "1           3      3                3               3         1           1   \n",
       "2           3      6                4               8         5           1   \n",
       "3           4      5                4               6         3           2   \n",
       "4           4      5                5               7         2           4   \n",
       "5           2      1                1               1         4           6   \n",
       "...       ...    ...              ...             ...       ...         ...   \n",
       "10964       5      8                8              10         7           1   \n",
       "10965       3      4                3               5         1           3   \n",
       "10966       4      5                1               7         2           3   \n",
       "10967       2      2                1               2         2           4   \n",
       "10968       5      8                6               8         1           5   \n",
       "\n",
       "       insur_1  insur_2  insur_3  insur_4  family_m  family_status  house  \\\n",
       "id                                                                          \n",
       "1            1        1        1        2         2              2      1   \n",
       "2            1        1        1        1         3              4      1   \n",
       "3            1        1        2        2         3              3      1   \n",
       "4            2        2        2        2         3              3      1   \n",
       "5            1        2        2        2         4              3      1   \n",
       "...        ...      ...      ...      ...       ...            ...    ...   \n",
       "10964        1        2        2        2         2              2      7   \n",
       "10965        1        2        2        2         4              3      1   \n",
       "10966        1        1        2        2         2              3      1   \n",
       "10967        1        1        2        2         2              3      1   \n",
       "10968        1        1        2        2         5              3      1   \n",
       "\n",
       "       car  invest_0  invest_1  invest_2  invest_3  invest_4  invest_5  \n",
       "id                                                                      \n",
       "1        2         0         1         0         0         0         0  \n",
       "2        2         0         1         0         0         0         0  \n",
       "3        2         0         1         0         0         0         0  \n",
       "4        1         0         1         0         0         0         0  \n",
       "5        1         0         1         0         0         0         0  \n",
       "...    ...       ...       ...       ...       ...       ...       ...  \n",
       "10964    2         0         1         0         0         0         0  \n",
       "10965    2         0         1         0         0         0         0  \n",
       "10966    2         0         1         0         0         0         0  \n",
       "10967    2         0         1         0         0         0         0  \n",
       "10968    2         0         1         0         0         0         0  \n",
       "\n",
       "[10968 rows x 20 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1[int_cols[40:60]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>invest_6</th>\n",
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       "      <td>6</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "<p>10968 rows × 20 columns</p>\n",
       "</div>"
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       "       invest_6  invest_7  invest_8  son  daughter  marital  f_edu  \\\n",
       "id                                                                   \n",
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       "...         ...       ...       ...  ...       ...      ...    ...   \n",
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       "10967         0         0         0    1         1        3      1   \n",
       "10968         0         0         0    1         2        3      1   \n",
       "\n",
       "       f_political  f_work_14  m_edu  m_political  m_work_14  status_peer  \\\n",
       "id                                                                          \n",
       "1                4          1      4            1          1            3   \n",
       "2                1          2      3            1          2            1   \n",
       "3                1          2      1            1          2            2   \n",
       "4                1          2      1            1          2            2   \n",
       "5                1         10      4            1         15            3   \n",
       "...            ...        ...    ...          ...        ...          ...   \n",
       "10964            1          2      2            1          2            1   \n",
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       "10966            1          2      1            1          2            2   \n",
       "10967            1          2      1            1          2            2   \n",
       "10968            1          1      6            1          1            2   \n",
       "\n",
       "       status_3_before  view  inc_ability  trust_1  trust_2  trust_3  trust_4  \n",
       "id                                                                             \n",
       "1                    2     4            3        4        2        4        4  \n",
       "2                    1     4            2        5        4        4        3  \n",
       "3                    1     4            2        3        3        3        3  \n",
       "4                    1     3            2        3        3        4        3  \n",
       "5                    2     3            2        4        3        3        3  \n",
       "...                ...   ...          ...      ...      ...      ...      ...  \n",
       "10964                1     4            3        4        4        4        4  \n",
       "10965                1     3            2        4        4        4        3  \n",
       "10966                1     4            2        4        4        4        4  \n",
       "10967                1     4            2        5        5        5        5  \n",
       "10968                2     4            2        4        4        4        4  \n",
       "\n",
       "[10968 rows x 20 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1[int_cols[60:80]]"
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      ],
      "text/plain": [
       "       trust_5  trust_6  trust_7  trust_8  trust_9  trust_10  trust_11  \\\n",
       "id                                                                       \n",
       "1            5        3        2        3        4         3        -8   \n",
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       "5            5        5        3        4        3         3         3   \n",
       "...        ...      ...      ...      ...      ...       ...       ...   \n",
       "10964        3        5        5        5        5         5         5   \n",
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       "10967        5        5        5        5        5         5         5   \n",
       "10968        5        4        4        4        4         3        -8   \n",
       "\n",
       "       trust_12  trust_13  neighbor_familiarity  public_service_1  \\\n",
       "id                                                                  \n",
       "1             4         1                     4                50   \n",
       "2             3         2                     3                90   \n",
       "3             3         1                     4                90   \n",
       "4             3         2                     3               100   \n",
       "5             3         2                     2                50   \n",
       "...         ...       ...                   ...               ...   \n",
       "10964         5         2                     5                50   \n",
       "10965         1         1                     3                60   \n",
       "10966        -8         2                     4                60   \n",
       "10967         5         5                     3                84   \n",
       "10968        -8         3                     5                90   \n",
       "\n",
       "       public_service_3  public_service_4  public_service_6  public_service_7  \\\n",
       "id                                                                              \n",
       "1                    50                50                30                50   \n",
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       "3                    75                79                90                90   \n",
       "4                    70                80                90                90   \n",
       "5                    50                50                50                50   \n",
       "...                 ...               ...               ...               ...   \n",
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       "10966                60                60                60                60   \n",
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       "       public_service_8  public_service_9  property_9  invest_9  birth_r  \n",
       "id                                                                        \n",
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       "\n",
       "[10968 rows x 20 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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  },
  {
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   "execution_count": 323,
   "metadata": {},
   "outputs": [
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       "       work_yrr  f_birth1  m_birth1\n",
       "id                                 \n",
       "1             7         0         0\n",
       "2             1        12        12\n",
       "3             0         0         0\n",
       "4             0         0         0\n",
       "5             0        12        12\n",
       "...         ...       ...       ...\n",
       "10964         2         8         7\n",
       "10965         0         0         0\n",
       "10966         0         0         0\n",
       "10967         0         0         0\n",
       "10968         0         0         0\n",
       "\n",
       "[10968 rows x 3 columns]"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1[int_cols[100:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_jin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10968.000000</td>\n",
       "      <td>10968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>163.905361</td>\n",
       "      <td>121.370988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.090577</td>\n",
       "      <td>23.150944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>164.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>170.000000</td>\n",
       "      <td>135.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>194.000000</td>\n",
       "      <td>260.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          height_cm    weight_jin\n",
       "count  10968.000000  10968.000000\n",
       "mean     163.905361    121.370988\n",
       "std        8.090577     23.150944\n",
       "min      100.000000     40.000000\n",
       "25%      158.000000    105.000000\n",
       "50%      164.000000    120.000000\n",
       "75%      170.000000    135.000000\n",
       "max      194.000000    260.000000"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# height_cm,weight_jin,身高体重信息\n",
    "data_1[['height_cm', 'weight_jin']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 身高体重以10记，比如身高是158，减去最小值100等于58//10+1 =6\n",
    "data_1.loc[:, 'height_cm'] = data_1.loc[:, 'height_cm'].apply(lambda x: (x-100)//10+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 体重也采取类似的操作\n",
    "data_1.loc[:, 'weight_jin'] = data_1.loc[:, 'weight_jin'].apply(lambda x: (x-40)//10+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>family_income</th>\n",
       "      <th>s_income</th>\n",
       "      <th>inc_exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.096800e+04</td>\n",
       "      <td>1.096800e+04</td>\n",
       "      <td>8.601000e+03</td>\n",
       "      <td>1.096800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.099577e+04</td>\n",
       "      <td>6.631525e+04</td>\n",
       "      <td>2.818087e+04</td>\n",
       "      <td>2.110477e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.002225e+05</td>\n",
       "      <td>2.835837e+05</td>\n",
       "      <td>1.755756e+05</td>\n",
       "      <td>2.589083e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.615000e+03</td>\n",
       "      <td>1.300000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>3.840000e+04</td>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>4.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.500000e+04</td>\n",
       "      <td>7.000000e+04</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999990e+06</td>\n",
       "      <td>9.999992e+06</td>\n",
       "      <td>8.999999e+06</td>\n",
       "      <td>1.000000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             income  family_income      s_income       inc_exp\n",
       "count  1.096800e+04   1.096800e+04  8.601000e+03  1.096800e+04\n",
       "mean   3.099577e+04   6.631525e+04  2.818087e+04  2.110477e+05\n",
       "std    2.002225e+05   2.835837e+05  1.755756e+05  2.589083e+06\n",
       "min    0.000000e+00   0.000000e+00 -3.000000e+00  0.000000e+00\n",
       "25%    1.615000e+03   1.300000e+04  0.000000e+00  1.000000e+04\n",
       "50%    1.500000e+04   3.840000e+04  1.200000e+04  4.000000e+04\n",
       "75%    3.500000e+04   7.000000e+04  3.000000e+04  8.000000e+04\n",
       "max    9.999990e+06   9.999992e+06  8.999999e+06  1.000000e+08"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# income,family_income, s_income, inc_exp, 进行分箱\n",
    "data_1[['income', 'family_income', 's_income', 'inc_exp']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cut(x):\n",
    "    if x<=1:\n",
    "        return 0\n",
    "    elif  1<x<1500:\n",
    "        return 1\n",
    "    elif  1500<x<=10000:\n",
    "        return 2\n",
    "    elif  10000<x<20000:\n",
    "        return 3\n",
    "    elif  20000<=x<40000:\n",
    "        return 4\n",
    "    elif  40000<=x<60000:\n",
    "        return 5\n",
    "    elif 60000<=x<100000:\n",
    "        return 6\n",
    "    else:\n",
    "        return 7\n",
    "    \n",
    "def fi_cut(x):\n",
    "    if x<=1:\n",
    "        return 0\n",
    "    elif  1<x<15000:\n",
    "        return 1\n",
    "    elif  15000<x<=40000:\n",
    "        return 2\n",
    "    elif  40000<x<=80000:\n",
    "        return 3\n",
    "    elif 80000<x<100000:\n",
    "        return 5\n",
    "    elif  100000<x<=200000:\n",
    "        return 6\n",
    "    else:\n",
    "        return 7\n",
    "    \n",
    "def inc_exp_cut(x):\n",
    "    return int(x//10000)+1\n",
    "\n",
    "data_1.loc[:, 'income_'] = data_1.loc[:, 'income'].apply(income_cut)\n",
    "data_1.loc[:, 'family_income_'] = data_1.loc[:, 'family_income'].apply(fi_cut)\n",
    "data_1.loc[:, 'inc_exp_'] = data_1.loc[:, 'inc_exp'].apply(inc_exp_cut)\n",
    "data_1.loc[:, 's_income_'] = data_1.loc[:, 's_income'].apply(income_cut)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10968.000000\n",
       "mean       115.983901\n",
       "std         90.882269\n",
       "min          0.000000\n",
       "25%         64.600000\n",
       "50%         97.800000\n",
       "75%        134.000000\n",
       "max       2400.000000\n",
       "Name: floor_area, dtype: float64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# floor_area 套内面积分箱\n",
    "data_1['floor_area'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "def floor_cut(x):\n",
    "    return int(x//50)+1\n",
    "data_1.loc[:,'floor_area_'] = data_1.loc[:, 'floor_area'].apply(floor_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1.drop(['income', 'family_income', 's_income', 'inc_exp', 'floor_area'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10968 entries, 1 to 10968\n",
      "Data columns (total 129 columns):\n",
      " #    Column                Non-Null Count  Dtype  \n",
      "---   ------                --------------  -----  \n",
      " 0    happiness             8000 non-null   float64\n",
      " 1    edu                   10968 non-null  int64  \n",
      " 2    edu_status            9399 non-null   float64\n",
      " 3    political             10968 non-null  int64  \n",
      " 4    join_party            1126 non-null   float64\n",
      " 5    property_0            10968 non-null  int64  \n",
      " 6    property_1            10968 non-null  int64  \n",
      " 7    property_2            10968 non-null  int64  \n",
      " 8    property_3            10968 non-null  int64  \n",
      " 9    property_4            10968 non-null  int64  \n",
      " 10   property_5            10968 non-null  int64  \n",
      " 11   property_6            10968 non-null  int64  \n",
      " 12   property_7            10968 non-null  int64  \n",
      " 13   property_8            10968 non-null  int64  \n",
      " 14   height_cm             10968 non-null  int64  \n",
      " 15   weight_jin            10968 non-null  int64  \n",
      " 16   health                10968 non-null  int64  \n",
      " 17   health_problem        10968 non-null  int64  \n",
      " 18   depression            10968 non-null  int64  \n",
      " 19   hukou                 10968 non-null  int64  \n",
      " 20   hukou_loc             10964 non-null  float64\n",
      " 21   media_1               10968 non-null  int64  \n",
      " 22   media_2               10968 non-null  int64  \n",
      " 23   media_3               10968 non-null  int64  \n",
      " 24   media_4               10968 non-null  int64  \n",
      " 25   media_5               10968 non-null  int64  \n",
      " 26   media_6               10968 non-null  int64  \n",
      " 27   leisure_1             10968 non-null  int64  \n",
      " 28   leisure_2             10968 non-null  int64  \n",
      " 29   leisure_3             10968 non-null  int64  \n",
      " 30   leisure_4             10968 non-null  int64  \n",
      " 31   leisure_5             10968 non-null  int64  \n",
      " 32   leisure_6             10968 non-null  int64  \n",
      " 33   leisure_7             10968 non-null  int64  \n",
      " 34   leisure_8             10968 non-null  int64  \n",
      " 35   leisure_9             10968 non-null  int64  \n",
      " 36   leisure_10            10968 non-null  int64  \n",
      " 37   leisure_11            10968 non-null  int64  \n",
      " 38   leisure_12            10968 non-null  int64  \n",
      " 39   socialize             10968 non-null  int64  \n",
      " 40   relax                 10968 non-null  int64  \n",
      " 41   learn                 10968 non-null  int64  \n",
      " 42   social_neighbor       9871 non-null   float64\n",
      " 43   social_friend         9871 non-null   float64\n",
      " 44   socia_outing          10968 non-null  int64  \n",
      " 45   equity                10968 non-null  int64  \n",
      " 46   class                 10968 non-null  int64  \n",
      " 47   class_10_before       10968 non-null  int64  \n",
      " 48   class_10_after        10968 non-null  int64  \n",
      " 49   class_14              10968 non-null  int64  \n",
      " 50   work_exper            10968 non-null  int64  \n",
      " 51   work_status           4029 non-null   float64\n",
      " 52   work_type             4030 non-null   float64\n",
      " 53   work_manage           4030 non-null   float64\n",
      " 54   insur_1               10968 non-null  int64  \n",
      " 55   insur_2               10968 non-null  int64  \n",
      " 56   insur_3               10968 non-null  int64  \n",
      " 57   insur_4               10968 non-null  int64  \n",
      " 58   family_m              10968 non-null  int64  \n",
      " 59   family_status         10968 non-null  int64  \n",
      " 60   house                 10968 non-null  int64  \n",
      " 61   car                   10968 non-null  int64  \n",
      " 62   invest_0              10968 non-null  int64  \n",
      " 63   invest_1              10968 non-null  int64  \n",
      " 64   invest_2              10968 non-null  int64  \n",
      " 65   invest_3              10968 non-null  int64  \n",
      " 66   invest_4              10968 non-null  int64  \n",
      " 67   invest_5              10968 non-null  int64  \n",
      " 68   invest_6              10968 non-null  int64  \n",
      " 69   invest_7              10968 non-null  int64  \n",
      " 70   invest_8              10968 non-null  int64  \n",
      " 71   son                   10968 non-null  int64  \n",
      " 72   daughter              10968 non-null  int64  \n",
      " 73   minor_child           9520 non-null   float64\n",
      " 74   marital               10968 non-null  int64  \n",
      " 75   marital_1st           10968 non-null  float64\n",
      " 76   marital_now           10968 non-null  float64\n",
      " 77   s_edu                 8601 non-null   float64\n",
      " 78   s_political           8601 non-null   float64\n",
      " 79   s_hukou               8601 non-null   float64\n",
      " 80   s_work_exper          8601 non-null   float64\n",
      " 81   s_work_status         3524 non-null   float64\n",
      " 82   s_work_type           3524 non-null   float64\n",
      " 83   f_edu                 10968 non-null  int64  \n",
      " 84   f_political           10968 non-null  int64  \n",
      " 85   f_work_14             10968 non-null  int64  \n",
      " 86   m_edu                 10968 non-null  int64  \n",
      " 87   m_political           10968 non-null  int64  \n",
      " 88   m_work_14             10968 non-null  int64  \n",
      " 89   status_peer           10968 non-null  int64  \n",
      " 90   status_3_before       10968 non-null  int64  \n",
      " 91   view                  10968 non-null  int64  \n",
      " 92   inc_ability           10968 non-null  int64  \n",
      " 93   trust_1               10968 non-null  int64  \n",
      " 94   trust_2               10968 non-null  int64  \n",
      " 95   trust_3               10968 non-null  int64  \n",
      " 96   trust_4               10968 non-null  int64  \n",
      " 97   trust_5               10968 non-null  int64  \n",
      " 98   trust_6               10968 non-null  int64  \n",
      " 99   trust_7               10968 non-null  int64  \n",
      " 100  trust_8               10968 non-null  int64  \n",
      " 101  trust_9               10968 non-null  int64  \n",
      " 102  trust_10              10968 non-null  int64  \n",
      " 103  trust_11              10968 non-null  int64  \n",
      " 104  trust_12              10968 non-null  int64  \n",
      " 105  trust_13              10968 non-null  int64  \n",
      " 106  neighbor_familiarity  10968 non-null  int64  \n",
      " 107  public_service_1      10968 non-null  int64  \n",
      " 108  public_service_2      10968 non-null  float64\n",
      " 109  public_service_3      10968 non-null  int64  \n",
      " 110  public_service_4      10968 non-null  int64  \n",
      " 111  public_service_5      10968 non-null  float64\n",
      " 112  public_service_6      10968 non-null  int64  \n",
      " 113  public_service_7      10968 non-null  int64  \n",
      " 114  public_service_8      10968 non-null  int64  \n",
      " 115  public_service_9      10968 non-null  int64  \n",
      " 116  property_9            10968 non-null  int64  \n",
      " 117  invest_9              10968 non-null  int64  \n",
      " 118  birth_r               10968 non-null  int64  \n",
      " 119  edu_yrr               8212 non-null   float64\n",
      " 120  work_yrr              10968 non-null  int64  \n",
      " 121  f_birth1              10968 non-null  int64  \n",
      " 122  m_birth1              10968 non-null  int64  \n",
      " 123  s_birth_r             8601 non-null   float64\n",
      " 124  income_               10968 non-null  int64  \n",
      " 125  family_income_        10968 non-null  int64  \n",
      " 126  inc_exp_              10968 non-null  int64  \n",
      " 127  s_income_             10968 non-null  int64  \n",
      " 128  floor_area_           10968 non-null  int64  \n",
      "dtypes: float64(22), int64(107)\n",
      "memory usage: 11.1 MB\n"
     ]
    }
   ],
   "source": [
    "data_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1 = data_1.astype(int, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "train_data = data_1[data_1['happiness'].notnull()]\n",
    "test_data = data_1[data_1['happiness'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class XGBClassifier in module xgboost.sklearn:\n",
      "\n",
      "class XGBClassifier(XGBModel, sklearn.base.ClassifierMixin)\n",
      " |  XGBClassifier(*, objective: Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType] = 'binary:logistic', use_label_encoder: bool = True, **kwargs: Any) -> None\n",
      " |  \n",
      " |  Implementation of the scikit-learn API for XGBoost classification.\n",
      " |  \n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  \n",
      " |      n_estimators : int\n",
      " |          Number of boosting rounds.\n",
      " |      use_label_encoder : bool\n",
      " |          (Deprecated) Use the label encoder from scikit-learn to encode the labels. For new\n",
      " |          code, we recommend that you set this parameter to False.\n",
      " |  \n",
      " |      max_depth :  Optional[int]\n",
      " |          Maximum tree depth for base learners.\n",
      " |      learning_rate : Optional[float]\n",
      " |          Boosting learning rate (xgb's \"eta\")\n",
      " |      verbosity : Optional[int]\n",
      " |          The degree of verbosity. Valid values are 0 (silent) - 3 (debug).\n",
      " |      objective : typing.Union[str, typing.Callable[[numpy.ndarray, numpy.ndarray], typing.Tuple[numpy.ndarray, numpy.ndarray]], NoneType]\n",
      " |          Specify the learning task and the corresponding learning objective or\n",
      " |          a custom objective function to be used (see note below).\n",
      " |      booster: Optional[str]\n",
      " |          Specify which booster to use: gbtree, gblinear or dart.\n",
      " |      tree_method: Optional[str]\n",
      " |          Specify which tree method to use.  Default to auto.  If this parameter\n",
      " |          is set to default, XGBoost will choose the most conservative option\n",
      " |          available.  It's recommended to study this option from the parameters\n",
      " |          document: https://xgboost.readthedocs.io/en/latest/treemethod.html.\n",
      " |      n_jobs : Optional[int]\n",
      " |          Number of parallel threads used to run xgboost.  When used with other Scikit-Learn\n",
      " |          algorithms like grid search, you may choose which algorithm to parallelize and\n",
      " |          balance the threads.  Creating thread contention will significantly slow down both\n",
      " |          algorithms.\n",
      " |      gamma : Optional[float]\n",
      " |          Minimum loss reduction required to make a further partition on a leaf\n",
      " |          node of the tree.\n",
      " |      min_child_weight : Optional[float]\n",
      " |          Minimum sum of instance weight(hessian) needed in a child.\n",
      " |      max_delta_step : Optional[float]\n",
      " |          Maximum delta step we allow each tree's weight estimation to be.\n",
      " |      subsample : Optional[float]\n",
      " |          Subsample ratio of the training instance.\n",
      " |      colsample_bytree : Optional[float]\n",
      " |          Subsample ratio of columns when constructing each tree.\n",
      " |      colsample_bylevel : Optional[float]\n",
      " |          Subsample ratio of columns for each level.\n",
      " |      colsample_bynode : Optional[float]\n",
      " |          Subsample ratio of columns for each split.\n",
      " |      reg_alpha : Optional[float]\n",
      " |          L1 regularization term on weights (xgb's alpha).\n",
      " |      reg_lambda : Optional[float]\n",
      " |          L2 regularization term on weights (xgb's lambda).\n",
      " |      scale_pos_weight : Optional[float]\n",
      " |          Balancing of positive and negative weights.\n",
      " |      base_score : Optional[float]\n",
      " |          The initial prediction score of all instances, global bias.\n",
      " |      random_state : Optional[Union[numpy.random.RandomState, int]]\n",
      " |          Random number seed.\n",
      " |  \n",
      " |          .. note::\n",
      " |  \n",
      " |             Using gblinear booster with shotgun updater is nondeterministic as\n",
      " |             it uses Hogwild algorithm.\n",
      " |  \n",
      " |      missing : float, default np.nan\n",
      " |          Value in the data which needs to be present as a missing value.\n",
      " |      num_parallel_tree: Optional[int]\n",
      " |          Used for boosting random forest.\n",
      " |      monotone_constraints : Optional[Union[Dict[str, int], str]]\n",
      " |          Constraint of variable monotonicity.  See tutorial for more\n",
      " |          information.\n",
      " |      interaction_constraints : Optional[Union[str, List[Tuple[str]]]]\n",
      " |          Constraints for interaction representing permitted interactions.  The\n",
      " |          constraints must be specified in the form of a nest list, e.g. [[0, 1],\n",
      " |          [2, 3, 4]], where each inner list is a group of indices of features\n",
      " |          that are allowed to interact with each other.  See tutorial for more\n",
      " |          information\n",
      " |      importance_type: Optional[str]\n",
      " |          The feature importance type for the feature_importances\\_ property:\n",
      " |  \n",
      " |          * For tree model, it's either \"gain\", \"weight\", \"cover\", \"total_gain\" or\n",
      " |            \"total_cover\".\n",
      " |          * For linear model, only \"weight\" is defined and it's the normalized coefficients\n",
      " |            without bias.\n",
      " |  \n",
      " |      gpu_id : Optional[int]\n",
      " |          Device ordinal.\n",
      " |      validate_parameters : Optional[bool]\n",
      " |          Give warnings for unknown parameter.\n",
      " |      predictor : Optional[str]\n",
      " |          Force XGBoost to use specific predictor, available choices are [cpu_predictor,\n",
      " |          gpu_predictor].\n",
      " |      enable_categorical : bool\n",
      " |  \n",
      " |          .. versionadded:: 1.5.0\n",
      " |  \n",
      " |          Experimental support for categorical data.  Do not set to true unless you are\n",
      " |          interested in development. Only valid when `gpu_hist` and dataframe are used.\n",
      " |  \n",
      " |      kwargs : dict, optional\n",
      " |          Keyword arguments for XGBoost Booster object.  Full documentation of\n",
      " |          parameters can be found here:\n",
      " |          https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst.\n",
      " |          Attempting to set a parameter via the constructor args and \\*\\*kwargs\n",
      " |          dict simultaneously will result in a TypeError.\n",
      " |  \n",
      " |          .. note:: \\*\\*kwargs unsupported by scikit-learn\n",
      " |  \n",
      " |              \\*\\*kwargs is unsupported by scikit-learn.  We do not guarantee\n",
      " |              that parameters passed via this argument will interact properly\n",
      " |              with scikit-learn.\n",
      " |  \n",
      " |          .. note::  Custom objective function\n",
      " |  \n",
      " |              A custom objective function can be provided for the ``objective``\n",
      " |              parameter. In this case, it should have the signature\n",
      " |              ``objective(y_true, y_pred) -> grad, hess``:\n",
      " |  \n",
      " |              y_true: array_like of shape [n_samples]\n",
      " |                  The target values\n",
      " |              y_pred: array_like of shape [n_samples]\n",
      " |                  The predicted values\n",
      " |  \n",
      " |              grad: array_like of shape [n_samples]\n",
      " |                  The value of the gradient for each sample point.\n",
      " |              hess: array_like of shape [n_samples]\n",
      " |                  The value of the second derivative for each sample point\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      XGBClassifier\n",
      " |      XGBModel\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, *, objective: Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType] = 'binary:logistic', use_label_encoder: bool = True, **kwargs: Any) -> None\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  evals_result(self) -> Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]]\n",
      " |      Return the evaluation results.\n",
      " |      \n",
      " |      If **eval_set** is passed to the `fit` function, you can call\n",
      " |      ``evals_result()`` to get evaluation results for all passed **eval_sets**.\n",
      " |      When **eval_metric** is also passed to the `fit` function, the\n",
      " |      **evals_result** will contain the **eval_metrics** passed to the `fit` function.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      evals_result : dictionary\n",
      " |      \n",
      " |      Example\n",
      " |      -------\n",
      " |      \n",
      " |      .. code-block:: python\n",
      " |      \n",
      " |          param_dist = {'objective':'binary:logistic', 'n_estimators':2}\n",
      " |      \n",
      " |          clf = xgb.XGBClassifier(**param_dist)\n",
      " |      \n",
      " |          clf.fit(X_train, y_train,\n",
      " |                  eval_set=[(X_train, y_train), (X_test, y_test)],\n",
      " |                  eval_metric='logloss',\n",
      " |                  verbose=True)\n",
      " |      \n",
      " |          evals_result = clf.evals_result()\n",
      " |      \n",
      " |      The variable **evals_result** will contain\n",
      " |      \n",
      " |      .. code-block:: python\n",
      " |      \n",
      " |          {'validation_0': {'logloss': ['0.604835', '0.531479']},\n",
      " |          'validation_1': {'logloss': ['0.41965', '0.17686']}}\n",
      " |  \n",
      " |  fit(self, X: Any, y: Any, *, sample_weight: Union[Any, NoneType] = None, base_margin: Union[Any, NoneType] = None, eval_set: Union[List[Tuple[Any, Any]], NoneType] = None, eval_metric: Union[str, List[str], Callable[[numpy.ndarray, xgboost.core.DMatrix], Tuple[str, float]], NoneType] = None, early_stopping_rounds: Union[int, NoneType] = None, verbose: Union[bool, NoneType] = True, xgb_model: Union[xgboost.core.Booster, str, xgboost.sklearn.XGBModel, NoneType] = None, sample_weight_eval_set: Union[List[Any], NoneType] = None, base_margin_eval_set: Union[List[Any], NoneType] = None, feature_weights: Union[Any, NoneType] = None, callbacks: Union[List[xgboost.callback.TrainingCallback], NoneType] = None) -> 'XGBClassifier'\n",
      " |      Fit gradient boosting classifier.\n",
      " |      \n",
      " |      Note that calling ``fit()`` multiple times will cause the model object to be\n",
      " |      re-fit from scratch. To resume training from a previous checkpoint, explicitly\n",
      " |      pass ``xgb_model`` argument.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X :\n",
      " |          Feature matrix\n",
      " |      y :\n",
      " |          Labels\n",
      " |      sample_weight :\n",
      " |          instance weights\n",
      " |      base_margin :\n",
      " |          global bias for each instance.\n",
      " |      eval_set :\n",
      " |          A list of (X, y) tuple pairs to use as validation sets, for which\n",
      " |          metrics will be computed.\n",
      " |          Validation metrics will help us track the performance of the model.\n",
      " |      eval_metric :\n",
      " |          If a str, should be a built-in evaluation metric to use. See doc/parameter.rst.\n",
      " |      \n",
      " |          If a list of str, should be the list of multiple built-in evaluation metrics\n",
      " |          to use.\n",
      " |      \n",
      " |          If callable, a custom evaluation metric. The call signature is\n",
      " |          ``func(y_predicted, y_true)`` where ``y_true`` will be a DMatrix object such\n",
      " |          that you may need to call the ``get_label`` method. It must return a str,\n",
      " |          value pair where the str is a name for the evaluation and value is the value\n",
      " |          of the evaluation function. The callable custom objective is always minimized.\n",
      " |      early_stopping_rounds :\n",
      " |          Activates early stopping. Validation metric needs to improve at least once in\n",
      " |          every **early_stopping_rounds** round(s) to continue training.\n",
      " |          Requires at least one item in **eval_set**.\n",
      " |      \n",
      " |          The method returns the model from the last iteration (not the best one).\n",
      " |          If there's more than one item in **eval_set**, the last entry will be used\n",
      " |          for early stopping.\n",
      " |      \n",
      " |          If there's more than one metric in **eval_metric**, the last metric will be\n",
      " |          used for early stopping.\n",
      " |      \n",
      " |          If early stopping occurs, the model will have three additional fields:\n",
      " |          ``clf.best_score``, ``clf.best_iteration``.\n",
      " |      verbose :\n",
      " |          If `verbose` and an evaluation set is used, writes the evaluation metric\n",
      " |          measured on the validation set to stderr.\n",
      " |      xgb_model :\n",
      " |          file name of stored XGBoost model or 'Booster' instance XGBoost model to be\n",
      " |          loaded before training (allows training continuation).\n",
      " |      sample_weight_eval_set :\n",
      " |          A list of the form [L_1, L_2, ..., L_n], where each L_i is an array like\n",
      " |          object storing instance weights for the i-th validation set.\n",
      " |      base_margin_eval_set :\n",
      " |          A list of the form [M_1, M_2, ..., M_n], where each M_i is an array like\n",
      " |          object storing base margin for the i-th validation set.\n",
      " |      feature_weights :\n",
      " |          Weight for each feature, defines the probability of each feature being\n",
      " |          selected when colsample is being used.  All values must be greater than 0,\n",
      " |          otherwise a `ValueError` is thrown.  Only available for `hist`, `gpu_hist` and\n",
      " |          `exact` tree methods.\n",
      " |      callbacks :\n",
      " |          List of callback functions that are applied at end of each iteration.\n",
      " |          It is possible to use predefined callbacks by using :ref:`callback_api`.\n",
      " |          Example:\n",
      " |      \n",
      " |          .. code-block:: python\n",
      " |      \n",
      " |              callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,\n",
      " |                                                      save_best=True)]\n",
      " |  \n",
      " |  predict(self, X: Any, output_margin: bool = False, ntree_limit: Union[int, NoneType] = None, validate_features: bool = True, base_margin: Union[Any, NoneType] = None, iteration_range: Union[Tuple[int, int], NoneType] = None) -> numpy.ndarray\n",
      " |      Predict with `X`.  If the model is trained with early stopping, then `best_iteration`\n",
      " |      is used automatically.  For tree models, when data is on GPU, like cupy array or\n",
      " |      cuDF dataframe and `predictor` is not specified, the prediction is run on GPU\n",
      " |      automatically, otherwise it will run on CPU.\n",
      " |      \n",
      " |      .. note:: This function is only thread safe for `gbtree` and `dart`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X :\n",
      " |          Data to predict with.\n",
      " |      output_margin :\n",
      " |          Whether to output the raw untransformed margin value.\n",
      " |      ntree_limit :\n",
      " |          Deprecated, use `iteration_range` instead.\n",
      " |      validate_features :\n",
      " |          When this is True, validate that the Booster's and data's feature_names are\n",
      " |          identical.  Otherwise, it is assumed that the feature_names are the same.\n",
      " |      base_margin :\n",
      " |          Margin added to prediction.\n",
      " |      iteration_range :\n",
      " |          Specifies which layer of trees are used in prediction.  For example, if a\n",
      " |          random forest is trained with 100 rounds.  Specifying ``iteration_range=(10,\n",
      " |          20)``, then only the forests built during [10, 20) (half open set) rounds are\n",
      " |          used in this prediction.\n",
      " |      \n",
      " |          .. versionadded:: 1.4.0\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      prediction\n",
      " |  \n",
      " |  predict_proba(self, X: Any, ntree_limit: Union[int, NoneType] = None, validate_features: bool = True, base_margin: Union[Any, NoneType] = None, iteration_range: Union[Tuple[int, int], NoneType] = None) -> numpy.ndarray\n",
      " |      Predict the probability of each `X` example being of a given class.\n",
      " |      \n",
      " |      .. note:: This function is only thread safe for `gbtree` and `dart`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array_like\n",
      " |          Feature matrix.\n",
      " |      ntree_limit : int\n",
      " |          Deprecated, use `iteration_range` instead.\n",
      " |      validate_features : bool\n",
      " |          When this is True, validate that the Booster's and data's feature_names are\n",
      " |          identical.  Otherwise, it is assumed that the feature_names are the same.\n",
      " |      base_margin : array_like\n",
      " |          Margin added to prediction.\n",
      " |      iteration_range :\n",
      " |          Specifies which layer of trees are used in prediction.  For example, if a\n",
      " |          random forest is trained with 100 rounds.  Specifying `iteration_range=(10,\n",
      " |          20)`, then only the forests built during [10, 20) (half open set) rounds are\n",
      " |          used in this prediction.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      prediction :\n",
      " |          a numpy array of shape array-like of shape (n_samples, n_classes) with the\n",
      " |          probability of each data example being of a given class.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from XGBModel:\n",
      " |  \n",
      " |  __sklearn_is_fitted__(self) -> bool\n",
      " |  \n",
      " |  apply(self, X: Any, ntree_limit: int = 0, iteration_range: Union[Tuple[int, int], NoneType] = None) -> numpy.ndarray\n",
      " |      Return the predicted leaf every tree for each sample. If the model is trained with\n",
      " |      early stopping, then `best_iteration` is used automatically.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array_like, shape=[n_samples, n_features]\n",
      " |          Input features matrix.\n",
      " |      \n",
      " |      iteration_range :\n",
      " |          See :py:meth:`xgboost.XGBRegressor.predict`.\n",
      " |      \n",
      " |      ntree_limit :\n",
      " |          Deprecated, use ``iteration_range`` instead.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_leaves : array_like, shape=[n_samples, n_trees]\n",
      " |          For each datapoint x in X and for each tree, return the index of the\n",
      " |          leaf x ends up in. Leaves are numbered within\n",
      " |          ``[0; 2**(self.max_depth+1))``, possibly with gaps in the numbering.\n",
      " |  \n",
      " |  get_booster(self) -> xgboost.core.Booster\n",
      " |      Get the underlying xgboost Booster of this model.\n",
      " |      \n",
      " |      This will raise an exception when fit was not called\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      booster : a xgboost booster of underlying model\n",
      " |  \n",
      " |  get_num_boosting_rounds(self) -> int\n",
      " |      Gets the number of xgboost boosting rounds.\n",
      " |  \n",
      " |  get_params(self, deep: bool = True) -> Dict[str, Any]\n",
      " |      Get parameters.\n",
      " |  \n",
      " |  get_xgb_params(self) -> Dict[str, Any]\n",
      " |      Get xgboost specific parameters.\n",
      " |  \n",
      " |  load_model(self, fname: Union[str, bytearray, os.PathLike]) -> None\n",
      " |      Load the model from a file or bytearray. Path to file can be local\n",
      " |      or as an URI.\n",
      " |      \n",
      " |      The model is loaded from XGBoost format which is universal among the various\n",
      " |      XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as\n",
      " |      feature_names) will not be loaded when using binary format.  To save those\n",
      " |      attributes, use JSON instead.  See: `Model IO\n",
      " |      <https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html>`_ for more\n",
      " |      info.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      fname :\n",
      " |          Input file name or memory buffer(see also save_raw)\n",
      " |  \n",
      " |  save_model(self, fname: Union[str, os.PathLike]) -> None\n",
      " |      Save the model to a file.\n",
      " |      \n",
      " |      The model is saved in an XGBoost internal format which is universal among the\n",
      " |      various XGBoost interfaces. Auxiliary attributes of the Python Booster object\n",
      " |      (such as feature_names) will not be saved when using binary format.  To save those\n",
      " |      attributes, use JSON instead. See: `Model IO\n",
      " |      <https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html>`_ for more\n",
      " |      info.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      fname : string or os.PathLike\n",
      " |          Output file name\n",
      " |  \n",
      " |  set_params(self, **params: Any) -> 'XGBModel'\n",
      " |      Set the parameters of this estimator.  Modification of the sklearn method to\n",
      " |      allow unknown kwargs. This allows using the full range of xgboost\n",
      " |      parameters that are not defined as member variables in sklearn grid\n",
      " |      search.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from XGBModel:\n",
      " |  \n",
      " |  best_iteration\n",
      " |  \n",
      " |  best_ntree_limit\n",
      " |  \n",
      " |  best_score\n",
      " |  \n",
      " |  coef_\n",
      " |      Coefficients property\n",
      " |      \n",
      " |      .. note:: Coefficients are defined only for linear learners\n",
      " |      \n",
      " |          Coefficients are only defined when the linear model is chosen as\n",
      " |          base learner (`booster=gblinear`). It is not defined for other base\n",
      " |          learner types, such as tree learners (`booster=gbtree`).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      coef_ : array of shape ``[n_features]`` or ``[n_classes, n_features]``\n",
      " |  \n",
      " |  feature_importances_\n",
      " |      Feature importances property, return depends on `importance_type` parameter.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      feature_importances_ : array of shape ``[n_features]`` except for multi-class\n",
      " |      linear model, which returns an array with shape `(n_features, n_classes)`\n",
      " |  \n",
      " |  intercept_\n",
      " |      Intercept (bias) property\n",
      " |      \n",
      " |      .. note:: Intercept is defined only for linear learners\n",
      " |      \n",
      " |          Intercept (bias) is only defined when the linear model is chosen as base\n",
      " |          learner (`booster=gblinear`). It is not defined for other base learner types, such\n",
      " |          as tree learners (`booster=gbtree`).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      intercept_ : array of shape ``(1,)`` or ``[n_classes]``\n",
      " |  \n",
      " |  n_features_in_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for `X`.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(xgb.XGBClassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:55:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, objective='multi:softprob', predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = train_data.drop('happiness', axis=1)\n",
    "y = train_data['happiness']\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test_data.drop('happiness', axis=1)\n",
    "y_pred = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = y_pred.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "subm1 = pd.DataFrame(t, columns=['happiness'])\n",
    "subm1.index = test.index\n",
    "subm1.to_csv('submission1.csv')  # 没有调参的结果0.63722"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "y=y.astype('int')\n",
    "params = {\n",
    "    'n_estimators': [100,200,400],\n",
    "    'learning_rate': [0.2, 0.3, 0.4],\n",
    "    'max_depth':[3, 4, 5]\n",
    "}\n",
    "model = xgb.XGBClassifier(objective='multi:softmax', n_jobs=8, verbosity=0)\n",
    "gridsearchcv = GridSearchCV(model, param_grid=params, scoring='accuracy', cv=5, n_jobs=-1, verbose=0)\n",
    "grid_result = gridsearchcv.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 100}"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = grid_result.best_estimator_\n",
    "model.fit(x,y)\n",
    "y_pred = model.predict(test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "subm2 = pd.DataFrame(y_pred.astype(int), columns=[\"happiness\"])\n",
    "subm2.index = test.index\n",
    "subm2.to_csv(\"submission2.csv\") # 0.61699"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['explained_variance', 'r2', 'max_error', 'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_absolute_percentage_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_root_mean_squared_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance', 'accuracy', 'top_k_accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted', 'balanced_accuracy', 'average_precision', 'neg_log_loss', 'neg_brier_score', 'adjusted_rand_score', 'rand_score', 'homogeneity_score', 'completeness_score', 'v_measure_score', 'mutual_info_score', 'adjusted_mutual_info_score', 'normalized_mutual_info_score', 'fowlkes_mallows_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted'])"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.metrics.SCORERS.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'n_estimators': [100,200,400],\n",
    "    'learning_rate': [0.2, 0.3, 0.4],\n",
    "    'max_depth':[3, 4, 5]\n",
    "}\n",
    "model = xgb.XGBRegressor(objective='reg:squarederror', n_jobs=8, verbosity=0)\n",
    "gridsearchcv = GridSearchCV(model, param_grid=params, scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=0)\n",
    "grid_result = gridsearchcv.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 100}"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- https://zhuanlan.zhihu.com/p/95304498 -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- https://blog.csdn.net/weixin_41988628/article/details/83098130 -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, colsample_bylevel=None,\n",
       "             colsample_bynode=None, colsample_bytree=None,\n",
       "             enable_categorical=False, gamma=None, gpu_id=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.2, max_delta_step=None, max_depth=3,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=8, num_parallel_tree=None, predictor=None,\n",
       "             random_state=None, reg_alpha=None, reg_lambda=None,\n",
       "             scale_pos_weight=None, subsample=None, tree_method=None,\n",
       "             validate_parameters=None, verbosity=None)"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = xgb.XGBRegressor(objective='reg:squarederror', n_jobs=8, n_estimators=100, learning_rate=0.2, max_depth=3)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.2, max_delta_step=0,\n",
       "             max_depth=3, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pred.astype(int)\n",
    "subm3 = pd.DataFrame(pred, columns=['happiness'])\n",
    "subm3.index = test.index\n",
    "subm3.to_csv(\"submission3.csv\") # 0.8445更差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'lambda':[0.1, 0.05, 0.2]\n",
    "}\n",
    "model = xgb.XGBClassifier(objective='multi:softmax', n_jobs=8, n_estimators=100, learning_rate=0.2, max_depth=3)\n",
    "gridsearchcv = GridSearchCV(model, param_grid=params, scoring='accuracy', cv=5, n_jobs=-1, verbose=0)\n",
    "grid_result = gridsearchcv.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lambda': 0.2}"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = grid_result.best_estimator_\n",
    "pred = model.predict(test)\n",
    "pred = pred.astype(int)\n",
    "subm3 = pd.DataFrame(pred, columns=['happiness'])\n",
    "subm3.index = test.index\n",
    "subm3.to_csv(\"submission4.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', lambda=0.2, learning_rate=0.2,\n",
       "              max_delta_step=0, max_depth=3, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, objective='multi:softprob', predictor='auto',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=0.200000003,\n",
       "              scale_pos_weight=None, subsample=1, tree_method='exact',\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.05}\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'gamma':[0.1,0.05,0.2]\n",
    "}\n",
    "model = xgb.XGBClassifier(objective='multi:softmax', n_jobs=8, n_estimators=100, learning_rate=0.2, max_depth=3,reg_lambda=0.2)\n",
    "gridsearchcv = GridSearchCV(model, param_grid=params, scoring='accuracy', cv=5, n_jobs=-1, verbose=0)\n",
    "grid_result = gridsearchcv.fit(x,y)\n",
    "print(grid_result.best_params_)\n",
    "model = grid_result.best_estimator_\n",
    "pred = model.predict(test)\n",
    "pred = pred.astype(int)\n",
    "subm3 = pd.DataFrame(pred, columns=['happiness'])\n",
    "subm3.index = test.index\n",
    "subm3.to_csv(\"submission5.csv\") # 0.60317"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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